{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"19f2bcb8-dfcf-45a4-b0a7-855bfd707cfa","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Engineering Manager","slug":"engineering-manager-3cdfd46c","description":"Overview\n\nInferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.\n\nAbout the Role\n\nWe're looking for an Engineering Manager to help scale a world-class, senior-heavy engineering organization working at the frontier of AI inference. You will lead a team of exceptional engineers across areas such as inference systems, performance, kernels, accelerators, and cloud infrastructure while creating the management capacity required for Inferact to keep growing quickly without sacrificing technical quality or execution speed.\n\nThis is a deeply technical management role. You will own team execution, hiring, coaching, prioritization, and operating rhythm, while staying close enough to the work to earn the trust of engineers solving hard systems problems. You will partner closely with engineering and company leadership to turn company priorities into clear technical direction, remove blockers, improve how the team works, and help every engineer do the highest-leverage work of their career.\n\n\n\nSkills and Qualifications\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, or similar.\n\n - Demonstrated experience managing strong software engineers in technically demanding systems, infrastructure, ML systems, distributed systems, or performance-oriented environments.\n\n - Strong technical credibility with the ability to understand architecture, ask useful questions, challenge assumptions, and help unblock senior engineers without becoming a bottleneck or overriding domain experts.\n\n - Experience owning the full people-management lifecycle, including hiring, onboarding, 1:1s, feedback, performance management, career development, team health, and retention.\n\n - Ability to translate ambiguous company priorities into clear team goals, sequencing, ownership, and execution plans while maintaining urgency in a fast-changing startup environment.\n\n - Track record of building effective engineering operating rhythms around planning, decision-making, technical reviews, cross-team coordination, and accountability without creating unnecessary process.\n\n - Strong judgment about when to dive into a technical problem, when to delegate, and how to create leverage for senior engineers rather than competing with them for ownership.\n\n - 2+ years of management experience with 10+ reports.\n\nPreferred qualifications:\n\n - Experience leading teams working on AI / ML infrastructure, model serving, distributed systems, GPU infrastructure, compilers, performance engineering, cloud infrastructure, or developer platforms.\n\n - Experience managing senior, staff, principal, research-adjacent, or PhD engineers in an environment where technical credibility and autonomy are especially important.\n\n - Experience scaling an engineering team through rapid hiring, including interviewer calibration, hiring decisions, onboarding systems, and thoughtful team design.\n\n - Familiarity with inference systems and adjacent technologies such as vLLM, SGLang, PyTorch, CUDA / Triton, Kubernetes, distributed serving, or accelerator infrastructure.\n\n - Experience partnering closely with founders or engineering executives in an early-stage company where priorities, team boundaries, and organizational needs evolve quickly.\n\nBonus points if you have:\n\n - Built or scaled a small, unusually high-talent engineering team where the technical surface area was specialized and the quality bar was extremely high.\n\n - Previously remained hands-on as an engineer or technical lead before moving into management and can still reason credibly about complex production systems.\n\n - Managed teams spanning multiple technical specialties, geographies, or time zones while preserving strong ownership and communication.\n\n - Helped establish engineering management practices from an early stage rather than inheriting a mature management system.\n\n - Contributed to open-source infrastructure, AI systems, developer tools, or technically ambitious products used by external engineers.\n\nLogistics\n\n - Location: This role is based in San Francisco, California and is in-office.\n\n - Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\n\n - Visa sponsorship: We sponsor visas on a case-by-case basis.\n\n - Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["cloud","llm","pytorch","gpu","mlops","distributed-systems","research"],"apply_url":"https://jobs.ashbyhq.com/inferact/98837c38-33a9-4085-97e7-1974dcf234a1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T11:23:31.216Z","expires_at":"2026-11-13T16:58:50.529662Z","created_at":"2026-10-14T15:22:31.292734Z","updated_at":"2026-10-14T16:58:50.533097Z","company_name":"Inferact","company_slug":"inferact","company_logo_url":"https://www.google.com/s2/favicons?domain=inferact.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/19f2bcb8-dfcf-45a4-b0a7-855bfd707cfa"},{"id":"7f073f29-8892-4033-ab21-b488ea264615","company_id":"d90e5b83-7941-4bd8-aace-5327c84c09c6","title":"Software Engineer, Applied Machine Learning","slug":"software-engineer-applied-machine-learning-b6a1a5f9","description":"fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.\n\nAs generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.\n\n\n\n\nABOUT THIS ROLE:\n\nWe are seeking a hands-on, production-focused Applied Machine Learning Engineer to take technical ownership of the model layer powering our next-generation generative media platform. In this role, you will bridge the gap between cutting-edge generative research and scalable, consumer-facing products.\n\nYou will split your time between extending SOTA open-source models with additional capabilities and helping maintain our fleet of generative model APIs.\nWhat you’ll do:\n\n - Novel Model Pipelines: Work with our post-training team to extend SOTA image, video, audio, and 3D models with additional capabilities and modalities. Develop novel approaches to model conditioning, generation, and editing, including both training-free methods and model fine-tuning.\n\n - Fine-Tuning: Leverage our massive GPU fleet to fine-tune generative models for novel capabilities. Build and maintain fine-tuning APIs that allow customers to customize models for their specific needs.\n\n - Architecture \u0026 Abstraction: Identify common patterns across the models we serve and develop reusable components, abstractions, and building blocks that accelerate the development of new model capabilities and inference pipelines.\n\n - Inference Optimization: Work hand-in-hand with our ML Performance \u0026 Optimization team to apply state-of-the-art inference techniques and best practices, ensuring models run efficiently with low latency, high throughput, and optimal GPU utilization.\n\n - Production Deployment: Build, deploy, and maintain scalable, reliable generative model APIs. Anticipate and resolve production challenges to ensure our models serve customers reliably at scale.\n\n - Customer Collaboration: Work directly with customers, including some of the world's largest e-commerce retailers and film and TV production studios, to develop novel solutions to their generative media needs.\n\nQualifications/Nice-to-haves:\n\n - Experience: 3+ years of professional experience as an Applied ML Engineer, with at least 1–2 years focused on generative media or computer vision.\n\n - Core Frameworks: Expert-level proficiency in Python and PyTorch.\n\n - Generative Media: Deep practical understanding of diffusion and flow-based generative models.\n\n - Open-Source Tooling: Hands-on experience working with open-weight model ecosystems, including Hugging Face, Diffusers, and related tooling.\n\n - Engineering Rigor: Ability to anticipate and solve challenges that arise when deploying ML models to production. Strong engineering judgment in designing systems that are scalable, reliable, secure, safe, and performant.\n\n - Training-Free Model Extensions: Experience designing and implementing training-free extensions to image, video, audio, or 3D generative models, such as novel conditioning methods, inference-time modifications, or new model capabilities.\n\n - Model Post-Training: Experience developing and executing custom post-training or fine-tuning approaches to extend generative models with additional capabilities.\n\n - Startup Experience: Track record of working in fast-paced startup environments or digital media and entertainment industries.\n\n - Ability to Ship: Demonstrated ability to independently take ambitious ML ideas from concept to production.\n\nWhat we offer at fal:\n\n - Interesting and challenging work\n\n - A lot of learning and growth opportunities\n\n - Health, dental, and vision insurance (US)\n\n - Regular team events and offsites\n\n\nCOMPENSATION\n\n - $180,000 - $230,000 + equity + comprehensive benefits package\n\n \n\n\n\nU.S. EQUAL EMPLOYMENT OPPORTUNITY INFORMATION:\n\nfal provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other classification protected by applicable law.","salary_min":180000,"salary_max":230000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["computer-vision","pytorch","fine-tuning","machine-learning"],"apply_url":"https://jobs.ashbyhq.com/fal-ai/c351a0de-1826-4968-a023-a5243aba2ec6/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T21:14:59.835Z","expires_at":"2026-11-13T16:57:32.416891Z","created_at":"2026-10-14T15:36:11.028291Z","updated_at":"2026-10-14T16:57:32.420794Z","company_name":"Fal","company_slug":"fal","company_logo_url":"https://www.google.com/s2/favicons?domain=fal.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7f073f29-8892-4033-ab21-b488ea264615"},{"id":"b34f4013-d975-493b-8a19-92a0a531c511","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Software Engineer, Machine Learning","slug":"staff-software-engineer-machine-learning-c84454fb","description":"About the Team \n DoorDash is building the world's most reliable on-demand, logistics engine for delivery. We are continuing to grow rapidly and expanding our Engineering offices globally! We are looking for Software Engineers, Machine Learning to build and maintain a large scale 24x7 global infrastructure system that powers DoorDash's 3-sided marketplace of Consumers, Merchants and Dashers.\n About the Role \n As a Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams.\n This role is hybrid with some in-office time expected and will report to an Engineering Manager. \n You're excited about this opportunity because you will... \n \n Develop advanced machine learning models to improve ads efficiency and quality.\n Design and build optimization algorithms for budget pacing and automated bidding to achieve various advertising goals. \n Establish a data-driven framework to understand how the bid density and market competitiveness would affect advertising value and platform revenue. \n Develop new data solutions (eg. embeddings and consumer profiles) to target the relevant audience.\n Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement.\n Build and extend the current data/ML infrastructure to empower Ads data applications including data analysis, ML modeling, and experimentation.\n Scale our systems and services to fuel the growth of our business.\n \n We're excited about you because you have... \n \n M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent.\n 8+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals.\n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL.\n Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, LightGBM, scikit-learn, Spark ML, or related.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $203,500 — $299,300 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","cloud","healthcare","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8092262","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:49:36Z","expires_at":"2026-11-13T17:03:28.820143Z","created_at":"2026-10-14T15:25:31.461436Z","updated_at":"2026-10-14T17:03:28.823202Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b34f4013-d975-493b-8a19-92a0a531c511"},{"id":"2bdabd0b-9451-4388-b502-2ba7d839c43f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, Drive","slug":"senior-machine-learning-engineer-drive-202e134e","description":"About the Team\n DoorDash Drive powers deliveries placed through merchants' own channels—including their websites, mobile apps, and phone orders—using DoorDash's logistics network. The Drive Machine Learning team builds the prediction and intelligence systems that power this business, including delivery and pickup time estimation, merchant prep-time prediction, order release optimization, logistics decision-making, and AI-powered delivery quality signals. Drive presents a unique machine learning challenge. Every merchant has different operational workflows, preparation patterns, and customer expectations, requiring models that generalize across millions of deliveries while adapting to highly diverse merchant behavior. Our team has significant opportunities to improve prediction accuracy, optimize logistics decisions, and build AI-native experiences that directly improve merchant, consumer, and dasher outcomes.\n About the Role\n As a Machine Learning Engineer on the Drive team, you'll own machine learning systems end-to-end—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration. Your work will span several high-impact problem areas:\n \n Build next-generation machine learning models for delivery ETA, pickup ETA, merchant prep-time estimation, and order release prediction that improve reliability for merchants and consumers.\n Develop deep learning models that leverage large-scale spatiotemporal, marketplace, and behavioral signals to improve prediction accuracy.\n Apply reinforcement learning and optimization techniques to improve logistics decision-making, assignment strategies, and marketplace efficiency.\n Build AI-native product experiences using large language models (LLMs) and vision-language models (VLMs). For example, transform pickup photos, item verification flows, receipts, and drop-off images into structured quality signals that help verify orders, prevent delivery defects, and improve issue resolution.\n Design and run rigorous online experiments, production monitoring, and model iteration to continuously improve performance.\n Partner closely with software engineers, product managers, data scientists, and platform teams to bring new machine learning capabilities into production at scale.\n You'll have the opportunity to work across traditional machine learning, deep learning, reinforcement learning, optimization, and multimodal AI while solving some of the most challenging logistics problems at DoorDash.\n \n You're excited about this opportunity because... \n \n You enjoy solving large-scale machine learning problems that directly impact millions of deliveries.\n You have a strong sense of ownership and enjoy taking models from research through production.\n You're comfortable working in ambiguous environments where experimentation and iteration drive product decisions.\n You care about both model quality and production reliability.\n You're excited to work across a diverse set of ML techniques—from neural networks and optimization to multimodal AI.\n You collaborate well across engineering, product, and data science teams.\n \n We’re excited about you because… \n \n 5+ years of industry experience building and shipping production machine learning systems with measurable business impact (Bachelor's, Master's, or PhD).\n Strong experience developing production machine learning models using modern deep learning frameworks such as PyTorch and distributed data processing technologies such as Spark and Airflow.\n Experience building, deploying, monitoring, and maintaining production ML systems end-to-end.\n Strong software engineering skills in Python and experience with modern ML infrastructure and tooling.\n Deep expertise in at least one of the following areas:\n \n Deep Learning\n Reinforcement Learning\n Optimization / Operations Research\n Large Language Models (LLMs) or Vision-Language Models (VLMs)\n \n Experience applying machine learning to estimation, ranking, prediction, optimization, or decision-making problems at production scale.\n Hands-on experience with LLMs or VLMs is a strong plus.\n Experience in logistics, marketplaces, or delivery platforms is helpful but not required.\n Proficiency using AI-assisted development tools (e.g. Claude Code, Codex, Cursor) throughout the software development lifecycle.\n You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.\n Compensation \n The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions.  Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about yo","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","deep-learning","reinforcement-learning","cloud","llm","generative-ai","healthcare","pytorch"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8221807","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:42:26Z","expires_at":"2026-11-13T17:03:28.706911Z","created_at":"2026-10-14T15:25:31.350903Z","updated_at":"2026-10-14T17:03:28.710264Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2bdabd0b-9451-4388-b502-2ba7d839c43f"},{"id":"78fae61b-8780-438a-b75a-b7c5778b3828","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Software Engineer, Machine Learning","slug":"senior-software-engineer-machine-learning-851e39b8","description":"About the Role \n As a Senior Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams. We expect Senior Engineers to be able to own projects end-to-end and mentor their junior and mid level peers.\n This role is hybrid with some in-office time expected and will report to an Engineering Manager. \n You're excited about this opportunity because you will... \n \n Develop advanced machine learning models to improve core parts of your teams scope.\n Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement.\n Work on creating novel ML \u0026 AI solutions that push both our business and the industry forward.\n Scale our systems and services to fuel the growth of our business.\n Completely disrupt logistics by tackling bleeding-edge, technical problems\n \n We're excited about you because you have... \n \n M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent.\n 5+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals.\n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL.\n Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, lightgbm, scikit-learn, Spark ML, or related.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $167,800 — $246,800 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.\n Our Commitment to Diversity and Inclusion\n We’re committed to growing and empowering a more inclusive","salary_min":167800,"salary_max":246800,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","healthcare","pytorch","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8143205","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:38:25Z","expires_at":"2026-11-13T17:03:28.716694Z","created_at":"2026-10-14T15:25:31.359543Z","updated_at":"2026-10-14T17:03:28.719518Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/78fae61b-8780-438a-b75a-b7c5778b3828"},{"id":"9b894a7b-e6ce-485c-b095-970a736af6dc","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Machine Learning Engineer, New Verticals Agentic Foundations","slug":"senior-machine-learning-engineer-new-verticals-agentic-foundations-a3f3a5d5","description":"About the Team\n Agentic Foundations, part of New Verticals ML, builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash, and proves it through flagship agent experiences such as the Ask grocery assistant. We operate as a closed loop: new models, evals, and representations make our agents better, and real-world agent usage surfaces the gaps that drive our next foundations work. \n About the Role\n We’re looking for a Senior Machine Learning Engineer to join Agentic Foundations with a primary focus on Vertical Agent Development. You will own the continuous improvement of our flagship agents, starting with the Ask Assistant, using an eval-driven loop to make them faster, more cost-efficient, and more reliable for millions of customers. You will also help us bring this approach to new agentic experiences for Merchants and Dashers, turning promising ideas into production-ready agents.\n This is a chance to work at the intersection of LLMs, agents, post-training, and evals on problems that ship. You will partner closely with engineers working on agentic memory, agent-compatible product representations, and post-training of small language models (SLMs), so that what we learn from production agents feeds directly back into our foundations, and what we build there makes our agents better.\n You will report into the engineering lead on our New Verticals AI/ML team. We expect this role to be hybrid with some time in-office and some time remote.\n You’re excited about this opportunity because you will…\n \n Raise the quality of our Consumer agents: build and refine our eval harness-optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context, and measure the impact on real customer tasks.\n Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can replace or augment larger LLMs.\n Launch new agents: take agentic opportunities for Merchants and Dashers from early exploration to production.\n Turn foundations into product impact: work with teammates on agentic memory, agent-compatible product representations, and post-training SLMs for steerable generative recommendation, and bring those capabilities into production agents.\n Shape the roadmap: partner with engineering, product, and business leaders to define an ML-driven strategy for our fast-growing grocery and retail delivery business.\n \n We’re excited about you because you have…\n \n 3+ years of industry ML experience, including hands-on work building and shipping LLM-based agents (tool use, context management, prompting, guardrails) and improving them with evals and data\n Experience with post-training or fine-tuning of open-weights models (e.g., SFT, preference optimization, or RL), ideally including small language models, and sound judgment on quality, latency, and cost trade-offs\n Strong foundation in NLP and machine learning, with proficiency in Python and frameworks such as PyTorch or TensorFlow\n M.S. or PhD in Computer Science, Statistics, Math, or another quantitative field, or equivalent practical experience, plus a collaborative, growth-minded approach and a drive for measurable impact\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United","salary_min":167800,"salary_max":246800,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","llm","fine-tuning","agents","cloud","pytorch","nlp","healthcare"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8279145","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:38:05Z","expires_at":"2026-11-13T17:03:28.711787Z","created_at":"2026-10-14T15:25:31.355292Z","updated_at":"2026-10-14T17:03:28.714838Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9b894a7b-e6ce-485c-b095-970a736af6dc"},{"id":"634e56a9-b38d-474b-847e-3dc417b38339","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Personalization","slug":"staff-machine-learning-engineer-personalization-d2d9e9a7","description":"About the Team \n Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop modern growth and personalization models that power DoorDash's growing retail and grocery business.\n About the Role \n We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning,  experience with solving end-user problems, and collaborate well with multi-disciplinary teams.\n You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).\n You’re excited about this opportunity because you will… \n \n Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space\n Partner with engineering and product leaders to help shape the product roadmap applying ML\n Mentor junior team members, and lead cross functional pods to create collective impact\n \n You can find out more on our ML blog here \n We’re excited about you because you have… \n \n 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. \n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field\n Expertise in applied ML for  Causal Inference and Recommendation Systems  -  both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms \u0026 LLMs is a plus.  \n Machine learning background in Python; experience with PyTorch or TensorFlow preferred.\n Ability to communicate technical details to nontechnical stakeholders\n You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down\n Desire for impact with a growth-minded and collaborative mindset\n \n \n Data Retention, Privacy \u0026 Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page . \n Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only\n We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.\n The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey \n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","cloud","deep-learning","fine-tuning","healthcare","pytorch","tensorflow","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8225006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:31:26Z","expires_at":"2026-11-13T17:03:28.796671Z","created_at":"2026-10-14T15:25:31.438732Z","updated_at":"2026-10-14T17:03:28.799759Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/634e56a9-b38d-474b-847e-3dc417b38339"},{"id":"8bf0267f-9f49-4c14-b3ad-dfe54d410a18","company_id":"1d9e5b7c-1619-4467-a0f1-cee763869454","title":"AI Software Engineer","slug":"ai-software-engineer-c9bf9f69","description":"At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nSnowflake is building the most powerful AI Data Cloud in the world — and Canada is a critical part of that mission. Our AI/ML engineering teams are delivering the intelligence layer that powers Cortex AI, large language model inference, AI functions, intelligent search, and the next generation of AI-powered experiences on the Snowflake platform.\n\nWe're looking for a Software Engineer — AI to join our growing Toronto engineering team. You'll design, build, and ship AI-powered systems and services that are used by thousands of organizations worldwide. If you're passionate about building production AI infrastructure at scale and want to work alongside world-class engineers on problems that matter, this is the role.\n\n\nAS A SOFTWARE ENGINEER — AI, YOU WILL:\n\n - Design, develop, and ship AI-powered features and services — working across model inference, AI functions, retrieval systems, and intelligent application experiences on the Snowflake platform\n\n - Build production-quality infrastructure for AI workloads — including model serving, embedding pipelines, retrieval-augmented generation (RAG), and AI-driven automation\n\n - Write clean, performant, and well-tested code in languages like Python, Java, C++, or Go — contributing to systems that operate at enterprise scale across Snowflake's multi-cloud infrastructure\n\n - Collaborate with ML researchers, senior engineers, and product managers to translate AI research and product requirements into robust, scalable engineering solutions\n\n - Contribute to system design — participate in design reviews, propose architectural improvements, and help shape the technical direction of AI services\n\n - Own your work end-to-end — from design and implementation through testing, deployment, monitoring, and operational health\n\n - Stay current on AI/ML advancements — evaluate new techniques, models, and tools and assess their applicability to Snowflake's platform and customer needs\n\n - Contribute to a strong engineering culture — through code reviews, documentation, knowledge sharing, and continuous improvement of team practices\n\n\nOUR IDEAL SOFTWARE ENGINEER — AI WILL HAVE:\n\nRequired:\n\n - 3+ years of software engineering experience building production systems, with a strong foundation in computer science fundamentals (data structures, algorithms, distributed systems)\n\n - Experience with AI/ML systems — you've worked on model serving, inference pipelines, LLM integration, embedding systems, or AI-powered application features in a production environment\n\n - Proficiency in one or more of: Python, Java, C++, or Go\n\n - Experience building distributed systems or cloud-native services — you understand the challenges of scale, reliability, and performance in a multi-cloud environment\n\n - Strong problem-solving skills and the ability to break down ambiguous problems into clear engineering tasks\n\n - Collaborative mindset — you work effectively with engineers, researchers, and product managers and communicate your ideas clearly\n\n - Ownership mentality — you take responsibility for your work from design through production and care about quality, reliability, and operational excellence\n\nPreferred:\n\n - Experience with LLM inference, fine-tuning, RAG architectures, or vector search at production scale\n\n - Familiarity with ML frameworks such as PyTorch, TensorFlow, or similar\n\n - Experience with cloud infrastructure (AWS, Azure, or GCP) and containerization (Docker, Kubernetes)\n\n - Background in data platforms, data warehousing, or SQL-based systems\n\n - Experience with CI/CD pipelines, automated testing, and production monitoring/observability\n\n - Contributions to open-source AI/ML projects or published research in AI/ML\n\n\nSNOWFLAKE VALUES WE'RE LOOKING FOR:\n\n - Get It Done — You ship working software. You move fast, iterate, and deliver results — not just prototypes.\n\n - Own It — You take end-to-end responsibility for your code, your systems, and your outcomes. If something breaks, you fix it.\n\n - Make Each Other the Best — You contribute to a culture of learning through thoughtful code reviews, knowledge sharing, and helping teammates grow.\n\n - Think Big — You don't just solve today's problem. You think about how AI at Snowflake should work at 10x scale and buil","salary_min":170000,"salary_max":500000,"location":"Toronto, Canada","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["rag","mlops","pytorch","fine-tuning","cloud","tensorflow","agents","llm"],"apply_url":"https://jobs.ashbyhq.com/snowflake/628f36e0-8554-4d5f-8d5e-a017a761ca54/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T00:00:58.144Z","expires_at":"2026-11-13T16:59:18.383644Z","created_at":"2026-10-14T15:22:50.102644Z","updated_at":"2026-10-14T16:59:18.386616Z","company_name":"Snowflake","company_slug":"snowflake","company_logo_url":"https://www.google.com/s2/favicons?domain=snowflake.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8bf0267f-9f49-4c14-b3ad-dfe54d410a18"},{"id":"6aceb6c9-aa17-4cd3-81ca-d26284538246","company_id":"5b08d89d-9846-4ef7-8b53-d07ef3ee6c19","title":"Software Engineer, ML Infrastructure","slug":"software-engineer-ml-infrastructure-f2fd5868","description":"About Arc Institute \n Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.\n Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.\n Our two Institute Initiatives reflect this model in action:\n \n Virtual Cell Initiative : Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.\n Alzheimer's Disease Initiative : Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.\n \n More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.\n About the position \n You'll join Arc's Infrastructure Engineering team and own the data and evaluation of ML infrastructure for Arc's virtual cell models. You'll start with two systems: our Model Performance Dashboard, which benchmarks those models, and the pipeline that turns single-cell data into their training sets. You'll set the technical direction for both and connect them, so every new dataset version is judged by what it does to the models. Later in your first year, the role widens into the engineering that makes the ML team's training and evaluation run fast and at scale, lab-in-the-loop infrastructure, validation frameworks for agent-written software, and Arc's rules for what research data we bring in, keep and share. The role consolidates ownership that is split across several teams today. \n Why this position could be the best job you’ve ever had \n \n You will contribute to building a virtual cell. Your evals show which data makes Arc's models better, and the answer shapes which experiments Arc's labs run next.\n You will own the platform end to end, and you work directly with the ML researchers and scientists who rely on it.\n \n About you \n \n You finish what you own, and you fix what's broken without being asked.\n You're curious about the biology, enough to learn single-cell perturbation data and argue about what an eval should measure. \n You're steady when priorities shift. Research is high-risk, so you test ideas early and change course when the evidence says to.\n You don't wait on scarce expert time. If proving a dataset's value means running the model evals yourself, you run them.\n \n In this position you will \n \n Own how Arc measures the performance of its virtual cell models, starting with the backend of our Model Performance Dashboard, which you'll take over within your first two months. \n Decide with the ML team which evaluation tools they need, then design and build them so researchers can evaluate and compare models themselves, and measure what each dataset version does to model performance.\n Redesign the pipeline that turns single-cell data into training sets for our virtual cell models, then run it with AI agents: agents find, process and curate datasets, and automated quality checks, not manual review, decide whether a dataset version is ready for model training.\n Own the lab-in-the-loop infrastructure that turns model performance results into the case for what Arc's labs should test next, working directly with the science leads who design those experiments . \n Build or buy validation frameworks (tests, evals, QC gates, policies) that let agent-written code and apps ship without line-by-line human review. Start with our team, where you'll also own how we work with coding agents, then extend them to apps that scientists across Arc build themselves.\n Own the engineering that makes the ML team's training and evaluation run fast, reliably and at scale. The ML team owns the research and the models. Once a model is ready to push, it enters the train-and-eval CI system you own, which feeds our Model Performance Dashboard. You'll also make multi-node training a supported path, track down distributed-training failures, and measure and raise throughput and GPU utilization (MFU) on clusters that Arc IT operates.\n \n Requirements \n \n Bachelor’s degree with 8 years of relevant experience, or Master’s degree with 6 years of relevant experience.\n You've owned a production data or ML platform that researchers depend on, end to end (pipelines, storage, versioning), and set its technical direction.\n You design and build systems with AI tools and agents, and you make their output verifiable with evals, QC gates or tests before anyone relies on it.\n ","salary_min":191000,"salary_max":237000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","healthcare","pytorch","distributed-systems","agents","machine-learning","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/arcinstitute/jobs/6222681004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:16:26Z","expires_at":"2026-11-13T17:03:53.841883Z","created_at":"2026-10-14T15:25:57.647484Z","updated_at":"2026-10-14T17:03:53.845803Z","company_name":"Arc Institute","company_slug":"arc-institute","company_logo_url":"https://www.google.com/s2/favicons?domain=arcinstitute.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6aceb6c9-aa17-4cd3-81ca-d26284538246"},{"id":"943a5e5f-5b9a-4c60-ae1f-a9319c87ec0e","company_id":"4bc4e268-7a05-4a65-a162-1688af546f7e","title":"Senior Machine Learning Programmer","slug":"senior-machine-learning-programmer-dd417989","description":"WHAT MAKES US EPIC?\n At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.\n Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.\n PROGRAMMING - SPECIAL PROJECTS\n What We Do \n The Special Projects team at Epic is responsible for executing high-impact projects that push the envelope to define the future of real-time graphics and gaming technology (The Matrix Awakens, Lumen in the Land of Nanite). In collaboration with the Unreal Engine team, we strive to put our technology and knowledge into the hands of users, empowering developers and content creators with the most powerful suite of real-time tools in the world.\n What You'll Do \n You will work with our team of applied research programmers at the intersection of computer vision, gameplay agents, natural language processing, and machine learning to create state-of-the-art tools to support game developers in our ecosystem with a focus on bots (trained gameplay agents, intelligent NPCs, companions, world modeling), as well as automation of a number key areas (including moderation, security, and QA testing). You will be responsible for the design, implementation and deployment of production-ready machine learning models that can be integrated in our ecosystem.\n In this role, you will \n \n Create and deliver production-ready, scalable and high-quality machine learning models and associated algorithms that can make actions in game environments\n Critically assess the effectiveness of such models and make recommendations for the ongoing roadmap\n Methodically understand and assess the effectiveness of new data channels and support collection/creation of new ones\n \n What we're looking for \n \n PhD in Computer Science, Mathematics or related field, or 3+ years of relevant industry experience\n Experience creating / applying machine learning algorithms for action modeling of bots behavior, vision-based or language-instructed, and deploying them as production-level systems in-game\n Expert, hands-on knowledge of:\n \n Deep learning, incl. but not limited to applications in the gaming industry\n Foundational models and vision and/or language, especially vision-language-action models, reinforcement learning agents, incl. local deployment and model post-training of open weights models\n Reinforcement learning, incl. experience with agentic systems, reward design, policy optimisation, evaluation, and building scalable training environments for agents\n Python and associated ML tools/frameworks (numpy, scipy, sklearn, pytorch)\n \n Experience with working collaboratively with other developers, following a flexible/agile/lean approach\n \n This role is open to multiple locations across North America \u0026 Europe (including NYC). \n Pay Transparency Information \n The expected annual base pay range(s) for this position are detailed below. Each base pay range is relevant only for individuals who are residents of or will be expected to work within the specified locale. Compensation varies based on a variety of factors, which include (but aren’t limited to) things such as skills and competencies, qualifications, knowledge, and experience. In addition to base pay, most employees are eligible to participate in Epic’s generous benefit plans and discretionary incentive programs (subject to the terms of those plans or programs). \n New York City Base Pay Range\n $232,666 — $341,243 USD \n ABOUT US\n Epic Games​ ​is a leading interactive entertainment company. For over 30 years we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.\n Like what you hear? Come be a part of something Epic! \n Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment Opportunity (EEO) Policy here .\n Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party (including recruitment or placement agencies) (i.e., a third party with whom we do not have a negotiated and validly executed agreement). We will ","salary_min":232666,"salary_max":341243,"location":"BLANK,BLANK,Multiple Locations","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","pytorch","deep-learning","reinforcement-learning","nlp","computer-vision","machine-learning"],"apply_url":"https://epicgames.com/careers/jobs/6221662004?gh_jid=6221662004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T19:46:26Z","expires_at":"2026-11-13T17:02:44.874623Z","created_at":"2026-10-14T15:24:47.963173Z","updated_at":"2026-10-14T17:02:44.878058Z","company_name":"Epic Games","company_slug":"epic-games","company_logo_url":"https://www.google.com/s2/favicons?domain=epicgames.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/943a5e5f-5b9a-4c60-ae1f-a9319c87ec0e"},{"id":"91788e64-435a-4da4-adc2-73888874d8f3","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Senior Technical Partnerships Manager","slug":"senior-technical-partnerships-manager-ad5320e1","description":"OWN YOUR INTELLIGENCE\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\nYOUR ROLE\n\nNo one builds the AI infrastructure stack alone. The GPU makers, cloud and datacenter providers, model labs, and enterprise platforms we work with are core to how we reach customers and how we ship. The partnerships that matter in this industry are not logos on a slide — they are shipped integrations, field teams that can actually sell and support a joint solution, and customers who win because both companies showed up together. Very few people know how to make those partnerships real at the technical and commercial level simultaneously.\n\nYou will own our most strategic technical alliances end to end: NVIDIA, hyperscalers, neoclouds, datacenter operators, and the next wave of partners across models, tooling, and enterprise. You will be the person a partner's engineering, product, and field teams trust to turn a signed MOU into something that moves pipeline and ships product.\n\nThe work sits at the intersection of technical depth and commercial execution. You will go deep enough on GPUs, networking, distributed training, and inference stacks to hold your own with partner engineers, and broad enough to run joint go-to-market, executive relationships, and a scalable partner program. You will scope proofs of concept with joint customers, build the enablement that lets partner sellers position us, secure marketplace and co-sell motions, and bring partner roadmaps back into our product and research decisions.\n\nThe partnerships you build will directly shape how Prime Intellect reaches the labs, enterprises, and research teams building on our stack — and how fast we can deliver compute, training, and environments to them.\n\n\n\n\nRESPONSIBILITIES\n\nPartner Relationship Management\n\n - Own 3–5 tier-1 technical partnerships (e.g., NVIDIA, hyperscalers, neoclouds, datacenter operators) as the single accountable lead\n\n - Build relationships across the partner org at every level: alliance managers, developer relations, product and engineering, field sales, and executives\n\n - Run quarterly business reviews and set joint annual plans with clear goals, owners, and metrics\n\n - Navigate partner programs (e.g., NVIDIA Inception, NPN, cloud marketplace and co-sell programs) to unlock credits, benefits, tiering, and visibility\n\n - Negotiate and manage partnership agreements, MOUs, and joint commitments with Legal and Finance\n\nJoint Customer Engagement\n\n - Lead and join joint customer calls with partner account teams, from discovery through technical deep dives and close\n\n - Act as the technical bridge in deals: map customer workloads to the joint solution, scope proofs of concept, and remove blockers\n\n - Coordinate escalations across both companies when joint customers hit issues\n\n - Build a pipeline of partner-sourced and partner-influenced opportunities, and report on it\n\nTechnical Integration \u0026 Product Feedback\n\n - Drive joint technical work: integrations, benchmarks, validated configurations, certifications, and optimizations on partner hardware and software\n\n - Get early access to partner roadmaps, hardware, and SDKs, and translate them into product opportunities for us\n\n - Bring partner and customer feedback into our product and research roadmaps, and advocate for our needs in the partner's roadmap\n\n - Write or co-author reference architectures, technical blog posts, and solution briefs\n\nTraining \u0026 Enablement\n\n - Build enablement content for partner field teams: pitch decks, battlecards, demo scripts, reference architectur","salary_min":250000,"salary_max":300000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","gpu","distributed-systems","agents","llm"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/3e11ec2c-34c1-45c8-a732-ab3001225f25/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T18:46:20.266Z","expires_at":"2026-11-13T16:58:27.616917Z","created_at":"2026-10-14T15:22:05.997825Z","updated_at":"2026-10-14T16:58:27.619663Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/91788e64-435a-4da4-adc2-73888874d8f3"},{"id":"edfac032-5e92-46fd-a2e9-6a04b912c3b3","company_id":"3e1a646a-409e-4841-b498-fcdfeb9ebf3f","title":"Applied Scientist","slug":"applied-scientist-3dc62a5a","description":"About Us \n Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day.\n We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn  and  X ,  and discover the projects we’re solving on our  Blog . Be sure to explore our  Interviewing Guide  to learn how to ace our interview process.\n About the Role \n We are looking for an Applied Scientist to solve challenging and open-ended problems in the domain of Creators and content contextual signals. As an Applied Scientist on Twitch's GTM Science team, you will use applied machine learning to tackle ambiguous business problems across problem areas such as Ads, Brand Safety, Creator Sponsorship and Content Understanding. You will lead high profile projects where you will use a wide toolbox of ML tools to handle multiple types of data, including user behavior, metadata, and user generated content such as text and video. You will collaborate with a team of passionate scientists, engineers, and product stakeholders to develop these models, prototype and put them into production, where they can help Twitch's creators and viewers succeed and build communities.\n You will report to our Senior Data Science Manager for GTM. This position is located in San Francisco, CA.\n You Will: \n \n Build machine learning products to enrich Twitch’s content understanding and help creators and viewers build and discover their communities. \n Propose, design, and oversee execution for high-impact projects that work backwards from customer problems to develop the right solution for the job. Know when to use a classical ML model versus a state-of-the-art one.\n Collaborate with cross-functional engineering and product teams to prototype and deploy your models into flexible data pipelines and ML-based services.\n Bring innovation and help drive science culture on the team by staying up to date and experimenting with new techniques in LLM, computer vision, inference and model evaluation. \n \n You Have: \n \n A degree in a quantitative field (Computer Science, Mathematics, Statistics, Physics, Engineering, or similar) with:\n \n 5+ years of hands-on industry experience with applied ML (Bachelor's), OR\n 3+ years of applied ML experience (Master's), OR\n 1+ year of applied ML experience (PhD)\n \n Experience with LLM model evaluations.\n Experience owning projects that involve numerous scientists and engineers.\n Experience working with both technical and non-technical partners and stakeholders.\n Expertise with: Python and SQL, modern open source ML libraries (Pytorch, Tensorflow, etc.). Developing software in AWS or similar cloud-computing services.\n \n Bonus Points \n \n PhD with a specialization in Machine Learning.\n Experience with computer vision, LLM inference, content understanding. \n 1+ years of experience working in GTM / Content / Creator orgs (not necessarily as a scientist).\n Familiarity with Twitch, its business, and its community.\n \n Perks \n \n Medical, Dental, Vision \u0026 Disability Insurance\n 401(k)\n Maternity \u0026 Parental Leave\n Flexible PTO\n Amazon Employee Discount\n \n   \n Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.  \n Job ID: TW9302\n The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life \u0026 AD\u0026D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits .\n US, CA, San Francisco\n $171,600 — $222,200 USD \n Twitch is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. \n Twitch values your privacy. Please consult our Candidate Privacy Notice , for information about how we collect, use, and disclose personal information of our candidates.","salary_min":171600,"salary_max":222200,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","payments","tensorflow","data-pipeline","computer-vision","llm","pytorch"],"apply_url":"https://job-boards.greenhouse.io/twitch/jobs/8872835002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T17:45:33Z","expires_at":"2026-11-13T16:58:02.931248Z","created_at":"2026-10-14T15:21:42.861233Z","updated_at":"2026-10-14T16:58:02.934126Z","company_name":"Twitch","company_slug":"twitch","company_logo_url":"https://www.google.com/s2/favicons?domain=twitch.tv\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/edfac032-5e92-46fd-a2e9-6a04b912c3b3"},{"id":"d6ccab06-8408-4826-b3a1-259ba586f060","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Machine Learning Platform - MDX","slug":"software-engineer-machine-learning-platform-mdx-445b9a46","description":"About the Team \n Come help us build the world's most reliable on-demand, logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.\n About the Role \n At DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search \u0026 Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale or Seattle.\n You’re excited about this opportunity because you will…\n \n Build a world-class ML platform where models are developed, trained, and deployed seamlessly\n Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases \n You will help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches\n You will work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day\n You will help design and drive directions for the centralized machine learning platform that powers all of DoorDash's business.\n Improve the reliability, scalability, and observability of our training and inference infrastructure.\n \n We’re excited about you because… \n \n B.S., M.S., or PhD. in Computer Science or equivalent\n Exceptionally strong knowledge of CS fundamental concepts and OOP languages\n 2+ years of industry experience in software engineering\n Prior experience building machine learning systems in production such as enabling data analytics at scale\n Prior experience in machine learning - you've developed and deployed your own models - even if these are simple proof of concepts\n Systems Engineering - you've built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems\n \n Nice To Haves \n \n Experience with challenges in real-time computing\n Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure\n Familiar with Pandas and Python machine learning libraries and deep learning frameworks such as PyTorch and TensorFlow\n Familiar with Spark, MLLib, Databricks,MLFlow, Apache Airflow, Dagster and similar related technologies.\n Familiar with large language models like GPT, LLAMA, BERT, or Transformer-based architectures\n Familiar with a cloud based environment such as AWS\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $130,600 — $192,000 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order fo","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["healthcare","pytorch","cloud","llm","deep-learning","distributed-systems","fine-tuning","tensorflow"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8264534","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:34Z","expires_at":"2026-11-13T17:03:28.767496Z","created_at":"2026-10-14T15:25:31.409011Z","updated_at":"2026-10-14T17:03:28.770438Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d6ccab06-8408-4826-b3a1-259ba586f060"},{"id":"bb310930-8502-4a93-ad2a-1918db8f50ca","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Principal Machine Learning Engineer, Applied AI ","slug":"principal-machine-learning-engineer-applied-ai-2ffeead3","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking a Principal Machine Learning Engineer, Applied AI  with deep expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts.\n Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product.\n This is a high-impact senior IC role for someone who brings deep, hands-on expertise in model post-training, evaluation, and production ML systems, built over many years of solving this exact class of problem.\n What You'll Be Building \n \n Apply deep, hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.\n Lead post-training efforts using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.\n Build and run evaluation loops that measure model quality, reliability, and customer fit, using patterns you've refined across many prior projects.\n Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.\n Partner with AI researchers to translate model advances into reliable, usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug complex model failures using traces, evaluations, customer context, and scientific feedback.\n Mentor engineers and share reusable patterns for model adaptation, evaluation, and deployment, drawing on lessons learned across a long track record of shipping ML systems. \n \n What You'll Need to Succeed \n \n Minimum 2-3 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before.\n Strong software engineering skills in Python and modern ML frameworks like PyTorch.\n A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before.\n Experience leading technical work across research and engineering teams.\n Deep familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. \n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Experience training MoE architectures.\n A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $252,000 — $336,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an en","salary_min":252000,"salary_max":336000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["pytorch","llm","reinforcement-learning","mlops","agents","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4400744009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T15:52:55Z","expires_at":"2026-11-13T17:03:03.861561Z","created_at":"2026-10-14T15:25:07.336193Z","updated_at":"2026-10-14T17:03:03.864854Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bb310930-8502-4a93-ad2a-1918db8f50ca"},{"id":"ec0beeba-9413-474e-88c1-ed020950d565","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior / Staff Machine Learning Engineer, Applied AI","slug":"senior-staff-machine-learning-engineer-applied-ai-80cee647","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking a Senior / Staff Machine Learning Engineer, Applied AI with expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts.\n Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product.\n This is the right role for someone who wants to go deep on model adaptation and evaluation, take real ownership of hard technical problems, and grow into broader scope over time. \n What You'll Be Building \n \n Apply hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.\n Run post-training work using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.\n Run evaluation loops that measure model quality, reliability, and customer fit.\n Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.\n Partner with AI researchers to translate model advances into reliable, usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug complex model failures using traces, evaluations, customer context, and scientific feedback.\n \n What You'll Need to Succeed \n \n Minimum 1-2 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before.\n Strong software engineering skills in Python and modern ML frameworks like PyTorch.\n A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before.\n Deep familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. \n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Experience training MoE architectures.\n A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $180,000 — $298,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.\n We’re All In \n Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran s","salary_min":180000,"salary_max":298000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","mlops","agents","llm","reinforcement-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4400741009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T15:52:17Z","expires_at":"2026-11-13T17:03:04.138761Z","created_at":"2026-10-14T15:25:07.633139Z","updated_at":"2026-10-14T17:03:04.141843Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ec0beeba-9413-474e-88c1-ed020950d565"},{"id":"db835cf3-0130-489a-9931-b9d8c638a18d","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Senior Engineer - Perception Modeling","slug":"senior-engineer-perception-modeling-3f6335e2","description":"On our Perception team, you have the opportunity to work with world-class ML engineers and research scientists, whose mission is to make self-driving vehicles a reality and to create a positive social impact. Our team works on the tech stack responsible for perceiving the dynamic scenarios, and further tracking and classifying objects around our robo-taxi. We are looking for engineers who are passionate about Level 4 autonomous driving technology, excited by intellectual challenges, and interested in pursuing career growth with a fast-growing company.\n What You'll Be Doing: \n \n Lead the architecture and development of advanced perception systems, focusing on, large-scale multi-modal foundation models, and robust knowledge generalization across vehicle generations\n Architect and scale synthetic data generation and sensor simulation capabilities to tackle long-tail edge cases, unseen off-log scenarios, and closed-loop evaluation\n Drive knowledge transfer from large teacher/foundation models to efficient onboard real-time perception models via advanced distillation and post-training techniques\n Productionize and deploy solutions onto autonomous vehicle fleets\n Provide technical leadership, mentorship, and guidance to senior and mid-level engineers on the team\n \n What We're Looking For: \n \n 3+ years of experience in Machine Learning, Computer Science, Robotics, or a related field\n Masters or Ph.D. in Machine Learning, Computer Science, Robotics, Applied Mathematics, Statistics, Physics or a related field; or equivalent industry experience\n In-depth understanding of common Machine Learning and Deep Learning algorithms\n Experience with designing, training, and analyzing neural networks for at least one of the following applications: object detection, semantic/instance segmentation, visual classification, motion/gesture recognition, sensor fusion, multitask learning, multi-object tracking, and/or end-to-end perception\n Experience with deep learning frameworks such as TensorFlow or PyTorch\n Fluency in Python, including standard scientific computing libraries and Python bindings development experience\n Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, testing methods\n Excellent communication and interpersonal skills\n Experience mentoring and leading others\n \n Preferred, but not required: \n \n Proven track record in designing, training, or fine-tuning Foundation Models (e.g., Vision-Language Models, Multimodal Transformers) for autonomous driving or robotic perception\n Deep expertise in World Models, generative video/lidar prediction, scene synthesis, and sensor simulation for closed-loop evaluation\n Strong experience with Knowledge Distillation and model compression techniques to transfer capability from large offline teacher models to lightweight edge deployments\n Demonstrated experience with Synthetic Data Generation (SDG) and multi-modal automated labeling systems to scale training data quality and volume\n Publications in premier computer vision or machine learning conferences (e.g., CVPR, ICML, NeurIPS, ICCV, ECCV)\n \n  \n #LI-BP1 \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  \n Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. \n Salary Range\n $172,000 — $229,000 USD \n Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. \n Our journey is always people first. \n We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.\n Higher purpose, greater impact. \n We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world diffe","salary_min":172000,"salary_max":229000,"location":"Boston, MA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["generative-ai","autonomous-vehicles","computer-vision","deep-learning","fine-tuning","robotics","pytorch","tensorflow"],"apply_url":"https://motional.com/open-positions/?gh_jid=8008804003#/8008804003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T17:15:40Z","expires_at":"2026-11-13T16:56:23.389669Z","created_at":"2026-10-14T15:20:30.46965Z","updated_at":"2026-10-14T16:56:23.392751Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/db835cf3-0130-489a-9931-b9d8c638a18d"},{"id":"389d3ac4-8c7f-4b52-810b-b05aeea8f4a8","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior, ML Engineer - Tracking","slug":"senior-ml-engineer-tracking-7090ab8b","description":"About the Company   \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. \n A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. \n Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.   \n Meet the Team \n Accurate and highly available information about other actors and objects is fundamental to the safe operation of autonomous trucks. Core capabilities depend on accurate tracking estimates to ensure safe and efficient vehicle behavior. \n The Tracking Team develops the ML models, state estimation algorithms, and production software responsible for associating and estimating the kinematics of other actors and objects in real time. Our solutions fuse information from multiple sensors and maintain robust performance despite sensor imperfections, environmental challenges, and system degradation. As part of this team, you will develop and deploy production-ready tracking solutions and help solve the challenges of bringing autonomous vehicle technology to real-world commercial applications. \n What You'll Do  \n \n Design, develop, and deploy production ML models for mulit-object tracking, including association, learned pose and kinematic estimation, and sensor fusion using camera, LiDAR, radar, and other vehicle sensors.   \n \n \n Develop scalable training and evaluation workflows using PyTorch, distributed training infrastructure, and large-scale real-world datasets.   \n \n \n Design and improve tracking and sensor fusion algorithms for robust vehicle pose and kinematics   \n \n \n Analyze large-scale vehicle data to characterize performance, identify failure modes, and drive model and system improvements.   \n \n \n Develop robust, efficient production software in modern C++ and Python across the full development lifecycle.   \n \n \n Define evaluation, verification, and validation strategies to ensure Tracking quality, robustness, and safety across diverse operating conditions.   \n \n \n Make technical design and architecture decisions, balancing model performance, computational efficiency, robustness, and production constraints.   \n \n \n Collaborate with perception, mapping, planning, controls, and platform teams to deliver integrated autonomous driving capabilities.   \n \n \n Provide technical leadership through design reviews, code reviews, mentoring, and development of engineering best practices.   \n \n   What You'll Need to Succeed  \n \n Bachelor’s degree in Computer Science, Software Engineering, Robotics, or a related field with 6+ years of relevant industry experience, or a Master’s degree with 3+ years of relevant industry experience, or a PhD with 1+ year of relevant industry experience.   \n \n \n Experience with AV or robotics perception systems (tracking, detection, SLAM, scene modeling, ect.).   \n \n \n Strong experience developing and deploying ML models for perception, localization, or sensor fusion domains.    \n \n \n Proficiency with PyTorch and modern ML tooling for training, inference, and optimization.    \n \n \n Solid understanding of 3D geometry, probabilistic estimation, coordinate transforms, and robotics fundamentals.    \n \n \n Demonstrated ability to work with large multimodal datasets and build scalable pipelines for processing, labeling, and evaluation.   \n \n \n Strong software engineering fundamentals in Python and C++, including algorithms, data structures, testing, debugging, and performance optimization.   \n \n \n Strong written and verbal communication skills and the ability to work effectively on cross-functional teams.   \n \n   Bonus Points  \n \n Experience with state estimation techniques such as factor graphs, Kalman filtering, nonlinear optimization, or related probabilistic estimation methods.   \n \n \n Familiarity with distributed computing tools such as Ray, Kubernetes, or similar orchestration frameworks.    \n \n \n Knowledge of embedded and real-time constraints for on-vehicle deployment.    \n \n \n Experience in simulation, synthetic data generation, and uncertainty-aware ML modeling.    \n \n \n Contributions to open-source robotics, perception, or ML frameworks.    \n Familiarity with functional safety standards and automotive development processes, including ISO 26262.   \n \n  \n Perks of Being a Full-time Torc’r \n Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:    \n \n A competitive compensation package that includes a bonus component and stock options   \n \n \n 100% paid medical, ","salary_min":177300,"salary_max":212800,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","autonomous-vehicles","pytorch","robotics","payments","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8765541002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T13:58:16Z","expires_at":"2026-11-13T16:56:17.79822Z","created_at":"2026-10-14T15:20:25.98843Z","updated_at":"2026-10-14T16:56:17.801031Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/389d3ac4-8c7f-4b52-810b-b05aeea8f4a8"},{"id":"56ebdfff-97fb-41ef-a385-6ed65f0a35ea","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Staff Applied AI Engineer - Enterprise AI Solutions","slug":"staff-applied-ai-engineer-enterprise-ai-solutions-9fe2164f","description":"About Snorkel \n Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes.  Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n The Role\n As an Applied AI Engineer, you’ll research and utilize state-of-the-art Gen AI and machine learning (ML) techniques to successfully deliver solutions to our customers. You will work directly with our customers to understand their business and technical needs and design and deliver AI solutions to solve them. You will also help define Snorkel’s Applied AI tooling by translating repeatable real-world challenges into reusable solution recipes, workflows, best practices, and platform-level capabilities that become part of Snorkel’s next generation of AI tooling. We move fast and are constantly prototyping and innovating new ways to deliver value to our customers. This position is ideal for someone who enjoys solving complex problems, bridging the gap between AI technology and business value, working directly with customers, keeping up-to date with AI research, and standardizing bespoke solutions into internal recipes and staying naturally curious about the infrastructure that underpin the Applied AI stack end-to-end.\n Main Responsibilities\n \n Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. This will involve designing custom approaches using state-of-the-art tools, with the goal of delivering real business value and informing the evolution of Snorkel’s tooling.\n Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes and agentic workflows.\n Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. A data- and evaluation-first mindset is essential for success in this role.\n Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects.\n Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup.\n Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices.\n Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best-suited for Snorkel AI.\n Serve as the voice of our customers for new AI paradigms, data science workflows, and share customer feedback to product teams.\n Conduct one-to-few and one-to-many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI.\n Annual travel up to 25%.\n \n Preferred Qualifications\n \n B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience.\n 8+ years of customer-facing experience in the design and implementation of AI/ML solutions.\n Proficiency in Python, including strong grounding in software engineering fundamentals (e.g., modular design, testing, profiling, packaging) and experience with modern Python constructs and libraries for type validation and typed data modeling (e.g., pydantic), building type-safe systems (e.g., mypy), testing (e.g., pytest), packaging and environment configuration (e.g., poetry), API and service frameworks (e.g., FastAPI), serialization and structured data handling (e.g., msgspec), and orchestration tooling relevant to ML deployment (e.g., Ray, Airflow).\n Expertise across the Applied AI stack, spanning classical ML libraries (e.g., scikit-learn), deep learning frameworks (e.g., PyTorch), foundation-model ecosystems (e.g., Hugging Face Transformers), vector/embedding tooling (e.g., FAISS), data processing frameworks (e.g., pandas, Spark), retrieval/RAG tooling (e.g., Chroma, Weaviate), synthetic dataset curation, evaluation workflows, and LLM orchestration, workflow, agent authoring tools (e.g., LlamaIndex, LangGraph, CrewAI).\n Experience leading strategic, customer-facing initiatives and collaborating with business stakeholders to ensure ML solutions drive successful business outcomes, with a strong focus on teaching and enablement.\n Outstanding presentation skills to technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos.\n Ability to work in a fast-paced environment and balance priorities across multiple projects at once.\n \n Co","salary_min":230000,"salary_max":360000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","embeddings","pytorch","deep-learning","fine-tuning","agents","generative-ai","rag"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6204368004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T03:25:50Z","expires_at":"2026-11-13T16:55:33.58289Z","created_at":"2026-10-14T15:19:23.699153Z","updated_at":"2026-10-14T16:55:33.585681Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/56ebdfff-97fb-41ef-a385-6ed65f0a35ea"},{"id":"6c602d86-d419-4ce2-929d-1dbc939432ed","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior Computer Vision Engineer","slug":"senior-computer-vision-engineer-29ad051c","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n About the Team\n The Counter Intrusion MSE team develops systems that provide force protection capabilities, monitoring the perimeter of secure areas, land or sea, for approaching people, vehicles, and vessels. We live in a world where security officers are increasingly overwhelmed by sensor data feeds. Our products leverage advanced sensor fusion and autonomy to seamlessly render activity in the environment to Lattice's common operating picture. This relieves operators of our system from a ton of burden and allows our customers to get more done with fewer personnel. Increasingly, Counter Intrusion MLEs are working on projects to bring customers' existing legacy security systems into the Lattice ecosystem. Key to this effort is building scalable software solutions so that we can service many customers without requiring bespoke software for each one. The Counter Intrusion team is responsible for the development, testing, deployment, and sustainment of our family of systems. We work closely with other teams from product, engineering, sales, logistics, operations, and mission success.\n About the Role\n We are looking for a Machine Learning Engineer to apply the latest research to solve our toughest problems. This role will be responsible for owning the whole machine learning stack for the Counter Intrusion team. You will design and train multi-sensor object detection models for perception on edge compute devices that push the boundaries of what's possible with our sensors. You will develop learning algorithms to optimize the behavior of autonomous systems. You will also be responsible for the end-to-end design, implementation, and performance of the ML stack, including the infrastructure for data collection and training. Your work will directly impact Anduril's ability to deliver cutting-edge defense technology, with the opportunity to identify and develop novel ML applications across our product portfolio.\n Responsibilities\n \n Propose and prototype innovative solutions to solve real-world problems, leveraging the latest state-of-the-art techniques in the field\n Develop and maintain core ML pipelines\n Train and deploy deep learning models for real-time applications\n Collaborate cross-functionally with camera, systems and labeling teams\n Curate datasets for evaluating performance and comparing performance trends over time\n Provide technical mentorship to other junior ML engineers\n \n Required Qualifications\n \n MS or PhD in Machine Learning, Robotics or Computer Science, with emphasis on Computer Vision\n BS in Computer Science, Machine Learning, Electrical Engineering, or related field\n 6+ years of experience developing, benchmarking and optimizing ML algorithms on large-scale datasets\n Strong Deep Learning and CV background\n Proficiency in C++ development in a Linux environment\n Experience with Python development and deep learning frameworks such as PyTorch, JAX and TensorFlow\n Experience deploying models with TensorRT and ONNX\n Optimize on-device inference and vision kernels across CPU/GPU/NPU\n Track record of developing and deploying CV models from R\u0026D to production\n Experience writing and maintaining automated continuous integration tests\n Knowledge of system profiling and tuning for latency, memory and power efficiency\n Ability to conduct experiments, ablation studies and create highly detailed reports\n Eligible to obtain and maintain a U.S. Secret security clearance\n \n Preferred Qualifications\n \n Experience in one or more of the following:\n \n Object Detection, Object Tracking, Instance Segmentation, Semantic Segmentation, Semantic Change Detection, Depth Estimation, Model Pruning and Compression\n \n Experience in one or more of the following:\n \n Visual Odometry, SLAM, Multi-view Geometry, Camera Models, RGB-D and LIDAR Sensor Fusion, Optical Flow\n \n Experience troubleshooting and analyzing remotely deployed software systems\n 1 year of experience in a technical leadership role\n US Hourly Range \n $220,000 — $292,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Hi","salary_min":220000,"salary_max":292000,"location":"Irvine, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["jax","computer-vision","deep-learning","tensorflow","robotics","pytorch","cloud","payments"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5256999007?gh_jid=5256999007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-05T17:08:54Z","expires_at":"2026-11-13T16:57:16.198459Z","created_at":"2026-10-14T15:21:01.365645Z","updated_at":"2026-10-14T16:57:16.201756Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6c602d86-d419-4ce2-929d-1dbc939432ed"},{"id":"b0dc07fc-9c77-4a68-a3b1-e54297cc4390","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Engineer, Ads ML Efficiency","slug":"staff-machine-learning-engineer-ads-ml-efficiency-469f1932","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the US or Canada.\n About the Team \n The ML Efficiency team builds the infrastructure, tooling, and optimization systems that enable machine learning engineers and researchers to train, evaluate, deploy, and operate models efficiently at scale. We focus on improving developer productivity, reducing infrastructure costs, increasing hardware utilization, and accelerating experimentation across the company’s ML ecosystem.\n Responsibilities \n \n Design and build systems that improve the efficiency of ML training and inference workloads.\n Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance.\n Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization.\n Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements.\n Build benchmarking frameworks and performance dashboards for training and serving systems.\n Optimize distributed training infrastructure, data pipelines, and model serving architectures.\n Lead cross-functional initiatives that improve the productivity of Reddit ML engineers.\n Drive technical strategy for ML platform scalability, reliability, and cost efficiency.\n \n Qualifications \n Required \n \n BS, MS, or PhD in Computer Science or a related field.\n 5+ years of software engineering experience.\n Strong proficiency in Python\n Profiency in at least one systems language (Go, C++, Rust, or Java) preferred\n Experience building distributed systems at scale.\n Experience with machine learning infrastructure, training systems, or model serving platforms.\n Deep understanding of performance engineering and systems optimization.\n Strong debugging and profiling skills.\n \n Preferred \n \n Experience with large-scale recommendation, ranking, generative AI, or foundation model systems.\n Experience with distributed training frameworks such as PyTorch Distributed, Ray, Tensorflow, Spark\n Familiarity with GPU architectures and performance analysis tools.\n Experience optimizing cloud infrastructure costs across large ML workloads.\n Contributions to internal platforms used by multiple ML teams.\n Experience with building real time ML inference applications\n \n What Success Looks Like \n \n ML engineers can move from idea to experiment faster.\n Training and inference costs decrease, performance increases, while model quality is maintained or improved.\n GPU utilization and cluster efficiency increase.\n Platform reliability improves as ML workloads scale.\n Teams spend less time managing infrastructure and more time building models.\n Average recommendation model size increases.\n \n Benefits: \n \n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Group Personal Pension Scheme with Employer match\n Private Medical and Dental Scheme\n Income Replacement Programs\n Bike to Work scheme\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave\n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ .\n To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.\n The base salary range for this position is:\n $230,000 — $322,000 USD \n In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.\n During the interview","salary_min":230000,"salary_max":322000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["generative-ai","mlops","cloud","data-pipeline","pytorch","tensorflow","distributed-systems","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8247280","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T17:48:26Z","expires_at":"2026-11-13T16:57:52.333083Z","created_at":"2026-10-14T15:21:32.499932Z","updated_at":"2026-10-14T16:57:52.336875Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b0dc07fc-9c77-4a68-a3b1-e54297cc4390"}],"page":1,"per_page":20,"total":967,"total_is_exact":true,"total_pages":49}