{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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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":"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":"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":"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"},{"id":"966ace03-2145-430c-ba1e-b86655a2101e","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Master's University Grad Machine Learning Engineer 2027 (USA)","slug":"masters-university-grad-machine-learning-engineer-2027-usa-bfd4b4f4","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n With more than 640 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers help build personalized experiences to help Pinners create a life they love. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.\n By applying to this role, you will be considered for multiple roles open across our various ML teams. Please only apply once within the USA or Canada as multiple applications may delay our recruitment process. \n This is a general posting for multiple roles open across our various ML teams. You will have the opportunity to work on a very large scope of problems in recommender systems, search, ads, ranking, natural language processing, graph representation learning, personalization, etc. \n As a Machine Learning Engineer at Pinterest, you’ll work on tackling new challenges in machine learning and artificial intelligence. As you kickstart your career at Pinterest, you’ll join our engineering teams as we maneuver through exponential growth and massive scale while building awesome products and features, creating visually rich experiences, spearheading the discovery problem, and pinpointing tomorrow’s engineering challenges. \n What you’ll do: \n \n Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems\n Collect, analyze, and synthesize findings from data and build intelligent data-driven model\n Write clean, efficient, and sustainable code\n Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search\n Design, build, and test models to predict engagement for notifications (push, emails, in-app notifications)\n Build content recommendation systems to power our push, email, and in-app notifications\n Work on state-of-the-art large-scale applied machine learning projects\n Scope and independently solve moderately complex problems\n \n What we’re looking for: \n \n Master's in Computer Science, ML, NLP, Statistics, Information Sciences or related field required\n Machine Learning experience (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc)\n Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data\n Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)\n Proficiency with AI-native engineering, including the design of agent-friendly codebases.\n High degree of autonomy in learning new agent-first development tools.\n Strong critical thinking when working with AI-generated suggestions, with a clear approach to validating correctness, performance, security, and maintainability.\n Comfort iterating on prompts, refining workflows, and adapting AI-assisted approaches based on the problem, context, and constraints.\n Experience in research and in solving analytical problems\n Strong communicator and team player. Being able to find solutions for open-ended problems.\n \n In-Office Requirement Statement: \n \n We let the type of work you do guide the collaboration style. That means we’re not always working in an office, but we continue to gather for key moments of collaboration and connection\n This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of our Pinterest offices. \n \n #LI-HYBRID \n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary","salary_min":146000,"salary_max":196000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["pytorch","tensorflow","computer-vision","search","nlp","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8140219","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T17:00:43Z","expires_at":"2026-11-13T16:57:51.091469Z","created_at":"2026-10-14T15:21:31.340179Z","updated_at":"2026-10-14T16:57:51.094368Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/966ace03-2145-430c-ba1e-b86655a2101e"},{"id":"c409314e-e707-469d-980c-8770d7b5d672","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"PhD University Grad Machine Learning Engineer (USA) ","slug":"phd-university-grad-machine-learning-engineer-usa-584220a7","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n With more than 640 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers help build personalized experiences to help Pinners create a life they love. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.\n By applying to this role, you will be considered for multiple roles open across our various ML teams. Please only apply once within the USA or Canada as multiple applications may delay our recruitment process. \n This is a general posting for multiple roles open across our various ML teams. You will have the opportunity to work on a very large scope of problems in recommender systems, search, ads, ranking, natural language processing, graph representation learning, personalization, etc. \n  \n As a Machine Learning Engineer at Pinterest, you’ll work on tackling new challenges in machine learning and artificial intelligence. As you kickstart your career at Pinterest, you’ll join our engineering teams as we maneuver through exponential growth and massive scale while building awesome products and features, creating visually rich experiences, spearheading the discovery problem, and pinpointing tomorrow’s engineering challenges. \n What you’ll do: \n \n Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems\n Collect, analyze, and synthesize findings from data and build intelligent data-driven model\n Write clean, efficient, and sustainable code\n Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search\n Design, build, and test models to predict engagement for notifications (push, emails, in-app notifications)\n Build content recommendation systems to power our push, email, and in-app notifications\n Work on state-of-the-art large-scale applied machine learning projects\n Scope and independently solve moderately complex problems\n \n What we’re looking for: \n \n PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field required\n Machine Learning experience (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc)\n Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data\n Mastery of at least one systems languages (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)\n Proficiency with AI-native engineering, including the design of agent-friendly codebases.\n High degree of autonomy in learning new agent-first development tools.\n Strong critical thinking when working with AI-generated suggestions, with a clear approach to validating correctness, performance, security, and maintainability.\n Comfort iterating on prompts, refining workflows, and adapting AI-assisted approaches based on the problem, context, and constraints.\n Experience in research and in solving analytical problems\n Strong communicator and team player. Being able to find solutions for open-ended problems.\n Preferred Qualifications: \n \n Publications in machine learning, AI, data science, data analytics, statistics, or related technical fields\n Interest in research and in applying ML to impactful real-world problems on the Pinterest product\n \n \n In-Office Requirement Statement: \n \n We let the type of work you do guide the collaboration style. That means we’re not always working in an office, but we continue to gather for key moments of collaboration and connection\n This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of our Pinterest offices.\n \n   \n #LI-HYBRID \n #LI-EB1\n At Pinte","salary_min":174078,"salary_max":229044,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["search","nlp","computer-vision","pytorch","tensorflow","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8140363","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T17:00:35Z","expires_at":"2026-11-13T16:57:51.109891Z","created_at":"2026-10-14T15:21:31.35309Z","updated_at":"2026-10-14T16:57:51.113365Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c409314e-e707-469d-980c-8770d7b5d672"},{"id":"5511badc-9403-413b-a158-ba1643b2f72a","company_id":"7f631da3-2c29-4575-a413-a9ed92f8af53","title":"Principal AI Architect","slug":"principal-ai-architect-5df377a8","description":"About the role:\n At Rubrik IT, we are transforming how the enterprise thinks and operates — building a \"thinking enterprise\" where intelligent, agentic systems reason across our data, tools, and workflows to drive speed, quality, and measurable business impact. As an AI Architect, you will be a pivotal technical leader driving AI Native solutions that make this vision real.\n You will operate where AI innovation, top-tier engineering, and business outcomes converge, acting as the technical bridge between complex business problems and cutting-edge AI execution. Leveraging our robust Enterprise AI Platform, you will translate ambiguous problems into concrete AI solution designs and ensure their successful deployment and measurable impact.\n This role is designed for systems-thinkers and orchestrators with an AI-first mindset — leaders who move fluidly between hands-on building  AI  solutions, and who know how to harness model APIs, data sources, and workflow layers to build practical technology that serves the business.\n What you'll do:\n \n Lead applied AI solution design and architecture, breaking down ambiguous business problems into concrete, actionable AI solution designs.\n Contribute to the detailed design of large-scale, distributed AI/ML systems, ensuring performance, reliability, and security.\n Design and improve retrieval, prompting, tool-calling, and orchestration patterns for internal use cases such as knowledge assistants, workflow automation, and decision support.\n Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI applications across teams.\n Drive the hands-on development and implementation of key AI components, supporting both traditional and Generative AI model development and deployment.\n Build and deploy AI-enabled internal workflows that connect enterprise systems, data sources, model APIs, and automation layers.\n Leverage AI-assisted development to accelerate implementation, bringing a strong 'editor' mindset to ruthlessly audit, review, and secure AI-generated code against our standards for reliability, security, observability, and documentation.\n Lead the implementation and continuous improvement of MLOps pipelines, including automated model training, versioning, deployment, and monitoring.\n Develop and maintain evaluation approaches for output quality, retrieval accuracy, latency, and failure modes, and use findings to improve system performance over time.\n Help identify where human review, controls, and escalation paths are required to support responsible deployment of AI-enabled systems.\n Apply sound engineering judgment to balance speed, usability, risk, and maintainability in production and near-production environments.\n Proactively collaborate with executive leadership, data science, engineering, and product stakeholders to translate business use-cases into scalable AI solutions.\n Lead rigorous problem formulation by partnering with R\u0026D,, Security, Data, HR, Finance, and business stakeholders — ensuring we apply AI to the right problems before translating needs into scalable technical solutions.\n Provide technical leadership and mentorship to other AI/ML engineers, fostering a culture of engineering excellence and hands-on experimentation.\n Contribute reusable components, playbooks, and patterns that reduce duplicate effort and improve speed across the engineering environment.\n Technical architecture and AI implementations directly to measurable business outcomes, recognizing that technology serves the business.\n Actively research and evaluate cutting-edge AI/ML techniques, algorithms, and models to identify opportunities for platform enhancement and new solution development.\n \n Experience you'll need:\n \n 10+ years of core Software/Systems Engineering architecture experience, including 4+ years dedicated to building and deploying applied AI/ML systems in production.\n Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s/Ph.D. is a plus).\n Direct hands-on experience with Generative AI technologies, including LLMs, Retrieval-Augmented Generation (RAG), context graphs, memory management, and agentic AI architectures.\n Strong programming skills in Python, Java, or Go. Deep proficiency with GenAI orchestration frameworks (e.g., LangGraph, LlamaIndex, AutoGen, Semantic Kernel) and a working knowledge of traditional ML frameworks (e.g., PyTorch, TensorFlow).\n Extensive hands-on experience with the modern AI data ecosystem, including Vector Databases (e.g., Pinecone, Weaviate) and Graph Databases (e.g., Neo4j).\n Experience with distributed systems architecture, event-driven streaming platforms, and cloud AI/ML platforms (e.g., GCP Vertex AI, AWS SageMaker, Azure OpenAI).\n Strong ability to work across data, model, application, and infrastructure layers, operating effectively with incomplete information and ambiguous problems.\n Experience integrating applications, data sources, or internal platfor","salary_min":254300,"salary_max":356300,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["distributed-systems","embeddings","tensorflow","agents","mlops","generative-ai","llm","rag"],"apply_url":"https://www.rubrik.com/company/careers/departments/job.8223656?gh_jid=8223656","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T17:55:54Z","expires_at":"2026-11-13T17:03:40.178154Z","created_at":"2026-10-14T15:25:42.349947Z","updated_at":"2026-10-14T17:03:40.181542Z","company_name":"Rubrik","company_slug":"rubrik","company_logo_url":"https://www.google.com/s2/favicons?domain=rubrik.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5511badc-9403-413b-a158-ba1643b2f72a"},{"id":"99144d66-5fc6-465c-ab90-f31887329dd3","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Software Engineer II - Ads Quality ","slug":"senior-software-engineer-ii-ads-quality-fe0ab6d9","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n As a Senior Software Engineer II in the Ads Quality team specializing in ML infrastructure, you'll work with a team of software and machine learning engineers to build state-of-the-art systems that optimize Ads performance throughout the ads serving funnel, from retrieval to pricing. You'll own the technical direction of our data, feature processing and model training/serving solutions, enabling next-generation modelling opportunities—from utilizing out of the box LLMs to building bespoke Foundation Models that powers all the Ads use cases. You will eliminate bottlenecks across the training and serving system to optimize stability, latency and cost. Building on Instacart's unique data and market position, these systems create value for advertising partners, improve consumer experience, and directly contribute to Instacart's overall revenue / business success.\n You'll have the unique opportunity to collaborate with a cross-functional team, turning diverse insights into powerful solutions that elevate advertising effectiveness and operational stability. Your expertise will steer the development of scalable training and serving infrastructure, driving performance and efficiency gains across our advertising platform. Together, we'll build an ads ecosystem that not only serves millions but also excels in delivering value to users and advertisers alike.\n Instacart Ads is transforming the online grocery shopping experience by seamlessly integrating advanced advertising solutions into our platform. The Ads Quality team is dedicated to delivering outstanding advertising outcomes by driving innovation and precision in response prediction, advertiser optimization, and ads auction processes. Our mission is to ensure relevance, fairness, and performance for all participants in the ad ecosystem. By leveraging terabytes of data and sophisticated machine learning algorithms, we build advanced predictive models that enhance user experience, drive advertiser success, and sustain a thriving ad ecosystem. We're also at the forefront of integrating Large Language Models (LLMs), unlocking powerful new capabilities that are redefining how brands connect with consumers in the digital grocery space. Our next major bet is a real-time sequential foundation model for ads—and building the training and serving infrastructure to make it possible is central to this role.\n About the Job \n \n Design, develop, and deploy machine learning infrastructure to tackle practical challenges in our complex marketplace while advancing the training and serving platform. Work with the company wide ML Foundation team to develop solutions to meet Ads unique needs.\n Optimize training and serving funnel bottlenecks—profiling real systems and driving meaningful wins in throughput, cost, and latency, from GPU/accelerator utilization to tail latency at serving time.\n Identify instabilities and shortcomings of our systems and operating procedures to deliver best in class availability.\n Build real-time feature processing infrastructure—low-latency pipelines that compute, join, and serve fresh features to models on the critical path of every ad request.\n Lead the infrastructure foundation for our real-time sequential foundation model, including online sequence feature stores, streaming update paths, efficient long-context serving, and training pipelines that keep pace with rapidly evolving data.\n Collaborate closely with product managers, data scientists, and MLEs to deeply understand business needs and co-design systems where product, model architecture and infrastructure constraints are solved together.\n Actively engage with diverse stakeholders to ensure that solutions are well-integrated and aligned with business goals.\n Mentor engineers, set technical direction, and conduct thorough code reviews to ensure high code qua","salary_min":192000,"salary_max":202500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["mlops","llm","tensorflow","generative-ai"],"apply_url":"https://instacart.careers/job/?gh_jid=8234282","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T21:04:42Z","expires_at":"2026-11-13T16:57:55.630268Z","created_at":"2026-10-14T15:21:35.780538Z","updated_at":"2026-10-14T16:57:55.633483Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/99144d66-5fc6-465c-ab90-f31887329dd3"},{"id":"44ef62a5-3b4a-4e72-8588-c72320556cd1","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Engineer, App Ads Modeling","slug":"staff-machine-learning-engineer-app-ads-modeling-8ac8cf82","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 Reddit has a flexible first workforce! \n We’re looking for a Staff-level Machine Learning Engineer to join Reddit’s Ads Ranking Org. This role is part of a broader effort to enhance Reddit’s ML-powered ad ranking systems through improved model architectures, contextual embeddings, and conversion-optimized modeling.\n The ideal candidate will bring strong end-to-end modeling expertise, a deep understanding of modern deep learning techniques, and a passion for building systems that drive measurable impact in ads marketplaces. While ads experience is a plus, strong modelers from recommendation systems, search relevance, or other performance-driven domains are encouraged to apply. This position will sit on the App Ads Modeling team and report directly into the Ranking Org.\n Responsibilities: \n \n Design and train advanced ML models (e.g., DNNs, transformers) to power Reddit Ads Ranking\n Develop and optimize features including embeddings, contextual signals, and cross-session behavior\n Work closely with product, infra, and data teams to drive end-to-end model deployment and performance analysis\n Mentor other MLEs and contribute to modeling best practices across the org\n Shape the long-term modeling vision across one or more domains (conversion, app ads, shopping, brand, etc.)\n \n Required Qualifications: \n \n 7+ years of industry experience, including several years in applied ML roles\n Strong experience with Deep Learning architectures and ML frameworks (TensorFlow, PyTorch)\n Strong background in recommendation systems, ads ranking, or similar domains\n Experience working with large-scale datasets and complex feature pipelines\n Strong problem-solving and experimentation skills\n State of the Art models for Ads or Recsys experience (ex: Transformers, Recsys)\n Knowledge about Recsys or Ads funnel\n Track record of delivering high-impact models in production settings\n Comfort collaborating with infra and data teams on feature serving and training pipelines\n \n Preferred Qualifications: \n \n Experience with conversion modeling, app ads, or performance/brand ads\n Experience in ads marketplaces at peer companies\n Publications, patents, or industry contributions in applied ML or ranking systems\n \n Benefits: \n \n 100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago)\n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\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 Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n  \n #LI-AJ1\n  \n  \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, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party fo","salary_min":230000,"salary_max":322000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","search","mlops","pytorch","tensorflow","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8187652","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T01:13:27Z","expires_at":"2026-11-13T16:57:52.343132Z","created_at":"2026-10-14T15:21:32.507357Z","updated_at":"2026-10-14T16:57:52.346192Z","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/44ef62a5-3b4a-4e72-8588-c72320556cd1"},{"id":"71918b47-6d14-4837-99c1-26bf0c070545","company_id":"01048ffd-9864-41e0-a719-14b849fbcbcd","title":"Site Reliability Engineer (High Performance Computing) ","slug":"site-reliability-engineer-high-performance-computing-86e8c5e0","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.\n SITE RELIABILITY ENGINEER (HIGH PERFORMANCE COMPUTING) \n SpaceX HPC is a shared compute platform used across the company — vehicle and structures simulation, machine learning, AI inference, and more. We support every program at SpaceX to design and operate the worlds most advanced rockets and satellites. This role exists to put a real Site Reliability Engineer operating model on these capabilities and accelerating the world class engineering at SpaceX: toil reduction, automation, observability, and a sustainable incident process.\n We are looking for a Site Reliability Engineer who wants to own everything from Linux machines and our Infrastructure as Code, storage, and user facing applications – the whole ecosystem as a product, not as a ticket queue. You do not need a prior HPC title. You do need production instincts — you have operated real infrastructure, you write code to delete toil, and you care about whether users can actually get work done, not just whether nodes ping. You’ll work alongside HPC systems engineers who design and commission clusters to help make them more reliable and provide world class services for world class engineers.\n Aerospace experience is not required. We value engineers who treat teammates with fairness and respect, who are self-critical, and who will take ownership of hard production problems.\n RESPONSIBILITIES: \n \n Participate in the team's on-call rotation; practice sustainable incident response and blameless postmortems\n Manage node lifecycle with infrastructure as code: OS images, firmware, configuration management, kernel and driver stack\n Build observability for both HPC administrators and end users — cluster, node, and storage health for operators, and job/workflow-level signal for the people running work on the platform\n Reduce toil with automation; split time between operating production systems and writing the software that makes that work smaller\n Sustainably manage resources, including compute and storage\n Lead capacity planning with users across the company: understand what they will need next, and turn that into a concrete picture of tomorrow's compute and storage\n Collaborate with HPC systems engineers and with engineers across all disciplines across the company on operable, maintainable infrastructure\n \n BASIC QUALIFICATIONS: \n \n Bachelor's degree in computer science, engineering, math, or a scientific discipline; OR 2+ years of professional experience operating production infrastructure in lieu of a degree\n 2+ years of experience with Linux operating systems in production\n 2+ years of experience operating production infrastructure (servers, services, or networks), including monitoring, debugging, and repairing what you own\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n 2+ years of professional experience in SRE, DevOps, or production infrastructure engineering\n Experience with monitoring and alerting (Prometheus, Grafana, Nagios, or similar)\n Experience deploying and maintaining configuration management or infrastructure as code (Ansible, Puppet, Terraform, or similar)\n Experience writing scripts/code (eg. Python or similar languages) to automate common tasks\n Experience with containers (Docker, Podman, Singularity/Apptainer)\n Experience with Kubernetes administration for on-premise deployment\n Experience with distributed or high-performance storage (VAST or similar), including capacity, performance, and lifecycle management\n Familiarity with HPC clusters, schedulers (Slurm, PBS, LSF), or GPU compute — not required; we will teach this\n Familiarity with scientific computing (CFD, FEA) and/or ML training workloads (PyTorch, TensorFlow, CUDA) and/or AI inference workloads\n Good understanding of version control, testing, continuous integration, build, deployment and monitoring\n Ability to communicate clearly with users, peers, and vendors in both incident and design settings\n Comfortable working with mission-critical and sensitive systems, with a sense of urgency appropriate to the responsibilities\n Eligibility for access to classified material up to TS/SCI with polygraph\n \n ADDITIONAL REQUIREMENTS: \n \n Position is based in Hawthorne, CA and is primarily on-site\n Must be able to participate in an on-call rotation\n Must be willing to work extended hours and weekends as needed for incidents, cluster bring-up, and time-critical failures\n \n COMPENSATION AND BENEFITS: \nPay Range: \nLevel 1: $125,000.00 - $160,000.00 Level 2: $145,000.00 - $195,000.00 \n Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.\n Base salary is just one part of your total rewa","salary_min":145000,"salary_max":195000,"location":"Hawthorne, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["gpu","tensorflow","pytorch","devops"],"apply_url":"https://boards.greenhouse.io/spacex/jobs/8831632002?gh_jid=8831632002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-23T15:58:59Z","expires_at":"2026-11-13T17:02:51.630554Z","created_at":"2026-10-14T15:24:54.667919Z","updated_at":"2026-10-14T17:02:51.633735Z","company_name":"SpaceX","company_slug":"spacex","company_logo_url":"https://www.google.com/s2/favicons?domain=spacex.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/71918b47-6d14-4837-99c1-26bf0c070545"},{"id":"7ef52fd3-3cc0-4c90-9f0a-f64a1b317d4e","company_id":"92df3417-f362-4f1a-9406-e34d8013b283","title":"Staff Machine Learning Engineer (Modeling), Support","slug":"staff-machine-learning-engineer-modeling-support-741ead08","description":"Block builds simple, powerful tools that make progress towards an economy that’s truly open to all. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we’re helping build a financial system that is open to everyone. Join us.\n The Role \n Block's Support ML Modeling team is a central driver of innovation in customer support experiences across our entire ecosystem—including Cash App, Square, and other business units. We are dedicated to advancing the state of intelligent, automated support through machine learning and generative AI. From customer-facing chatbots to smart internal tools for agents, our team builds high-impact, scalable systems that improve support quality, efficiency, and accessibility.\n We're building the future of support at Block: one powered by AI, voice interfaces, and smart automation. We're looking for candidates with a passion for intelligent systems, practical ML experience, and a desire to build product-driven solutions. Our ideal candidate is a leader in the field, with a proven track record of building ML systems that can accelerate our ability to scale our conversational AI initiatives. \n You Will \n \n Lead the end-to-end delivery of multiple ML initiatives, from planning and design through rollout, documentation, and long-term maintenance\n Partner strategically with risk, product, engineering, design, and operations leaders to define and drive long term ML roadmaps, informed by domain expertise and industry trends\n Guide the team’s direction, identifying new ML/AI opportunities and advising leadership on strategic tradeoffs and opportunities\n Drive R\u0026D efforts exploring next-generation chatbot architectures using LLMs, RAG, fine-tuning, and real-time inference\n Design, deploy, and maintain ML models powering conversational agents including support chatbots across Cash App, Square, and other Block products\n Develop ML-powered tools and real time recommendation systems that enhance support agent effectiveness and customer outcomes\n Act as the technical representative for your team’s systems and programs, clearly communicating work and results to stakeholders, cross-functional partners, and external audiences\n \n You Have \n \n 10+ years of experience in machine learning, applied AI, or product ML roles, with deep technical expertise\n Demonstrated leadership capabilities, with the ability to influence and align cross-functional teams while directly shaping the work of peers through communication, context sharing, and technical guidance\n A track record of delivering organizational wide impact, shaping systems, frameworks, or initiatives that raise the bar across multiple teams or functions\n Proven ability to ship end to end ML features from problem framing through deployment and long-term maintenance\n Demonstrated experience with language models, dialog systems, or generative AI in production\n Strong foundation in NLP, deep learning, or ML infrastructure best practices.\n Excellent communication skills, with the ability to represent the team’s work to leadership and stakeholders\n Enthusiasm for R\u0026D and pushing the boundaries of applied AI to transform conversational systems\n \n Technologies We Use and Teach \n \n Python, PyTorch, TensorFlow, or JAX\n \n We're working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.\n We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible.  Want to learn more about what we're doing to build a workplace that is fair and square? Check out our   I+D page .\n \n \n \n Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.\n ","salary_min":276800,"salary_max":415200,"location":"Seattle, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","fine-tuning","generative-ai","deep-learning","cloud","nlp","pytorch","tensorflow"],"apply_url":"http://block.xyz/careers/jobs/5431987008?gh_jid=5431987008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-22T21:19:49Z","expires_at":"2026-11-13T16:58:11.265472Z","created_at":"2026-10-14T15:21:51.289264Z","updated_at":"2026-10-14T16:58:11.268263Z","company_name":"Block","company_slug":"block","company_logo_url":"https://www.google.com/s2/favicons?domain=block.xyz\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7ef52fd3-3cc0-4c90-9f0a-f64a1b317d4e"},{"id":"2424c1ce-b300-4939-8187-d3ededb9970b","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Research Scientist, World Model Post-Training","slug":"research-scientist-world-model-post-training-504a059c","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.\n In this hybrid role, you will report to a Software Engineering Manager.\n You will: \n \n Conduct applied world model research and development\n Post-train Waymo’s world model\n Design compelling experiments by training and evaluating large generative models\n Present results to peers and leadership\n Leverage AI to write high quality code, unit tests, and documentation, and automate workflows\n \n You have: \n \n Masters or PhD in deep learning in generative / world modeling\n 3+ years of experience with modern deep learning frameworks (e.g., JAX, TensorFlow 2, Pytorch)\n Experience with diffusion / flow matching models\n Experience with reinforcement learning\n Experience training large foundational models\n \n We prefer: \n \n Experience with post-training of foundational models\n Experience with reinforcement learning for diffusion / flow matching models\n Experience with distillation\n Experience with autonomous vehicles\n \n  \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","pytorch","autonomous-vehicles","deep-learning","tensorflow","research"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8222098","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-22T16:48:50Z","expires_at":"2026-11-13T16:55:54.995986Z","created_at":"2026-10-14T15:20:04.436747Z","updated_at":"2026-10-14T16:55:54.998704Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2424c1ce-b300-4939-8187-d3ededb9970b"},{"id":"29745dc6-374a-4c6c-b0f0-b5e2b47ce31f","company_id":"5ce35db1-ec27-4df5-8ea1-167a8e9efac6","title":"Staff Software Engineer, Machine Learning (Consumer Revenue)","slug":"staff-software-engineer-machine-learning-consumer-revenue-2326d230","description":"Discord has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: play video games. Discord plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and after playing games.\n We're looking to hire a Staff Machine Learning Engineer on our Consumer Revenue ML team. This team applies Machine Learning across Discord's core revenue surfaces — Shop, Nitro, Server Subscriptions, and Gifting — building the ranking, targeting, and recommendation systems that connect users to the right products, subscriptions, and content. If this excites you and you’ve led org-wide initiatives in the past, keep reading! \n What You'll Be Doing \n \n 8+ years of experience in applied Machine Learning, inclusive Ph.D. or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.\n Strong expertise in applied deep learning and mainstream RecSys model architecture (e.g. two-tower, transformer-based models, multi-task learning).\n Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.\n A track record of building ML systems from 0→1 in ambiguous, early-stage environments, and taking them to production at scale.\n Strong product and business intuition, with the ability to translate experiment results into roadmap decisions.\n Excellent communication and collaboration skills — able to lead cross-functional technical initiatives across multiple verticals and keep stakeholders educated and aligned.\n The ability to thrive in ambiguous environments, energized by open-ended, technically challenging problems.\n \n What you should have \n \n Built internal ML platform/tooling (shared data standards, targeting endpoints, recommender libraries) adopted by multiple product teams.\n Familiarity with personalized marketing systems — lifecycle targeting, audience segmentation and lookalikes, campaign optimization.\n Deep expertise in distributed training (e.g. PyTorch on GPU, Ray, Anyscale) and large-scale data processing pipelines (e.g. Chronon, Spark, Flink).\n \n   \n This position is US-based and can be remote but if you live in the Bay Area, you're welcome to work from our beautiful SF office. \n The US base salary range for this full-time position is $272,000 to $340,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits. \n Why Discord?  Discord plays a uniquely important role in the future of gaming. We're a multiplatform, multigenerational and multiplayer platform that helps people deepen their friendships around games and shared interests, and helps developers build and grow their businesses. We believe games give us a way to have fun with our favorite people, whether listening to music together or grinding in competitive matches for diamond rank. Join us in our mission! Your future is just a click away! \n Discord is committed to inclusion and providing reasonable accommodations during the interview process. We want you to feel set up for success, so if you are in need of reasonable accommodations, please let your recruiter know.\n Please see our Applicant and Candidate Privacy Policy for details regarding Discord’s collection and usage of personal information relating to the application and recruitment process by clicking  HERE.","salary_min":272000,"salary_max":340000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["deep-learning","pytorch","tensorflow","distributed-systems","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/discord/jobs/8802354002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-22T00:35:05Z","expires_at":"2026-11-13T17:03:09.184645Z","created_at":"2026-10-14T15:25:12.638204Z","updated_at":"2026-10-14T17:03:09.187768Z","company_name":"Discord","company_slug":"discord","company_logo_url":"https://www.google.com/s2/favicons?domain=discord.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/29745dc6-374a-4c6c-b0f0-b5e2b47ce31f"},{"id":"1ae30773-57b7-45fa-826d-8ee7eb1cb372","company_id":"5fac52d7-9b0b-4990-80a2-e2949dd0af1d","title":"Staff Software Engineer, Machine Learning","slug":"staff-software-engineer-machine-learning-f1dfa719","description":"Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.\n  \n Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.\n  \n With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in  Deloitte’s Fast 500  (four years running!),  LinkedIn’s Top Startups ,  Forbes’ Cloud 100 (five years running!),  Inc.’s Best Workplaces , and the  Human Rights Campaign Foundation's Corporate Equality Index !\n About the Role \n Our Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. As a Senior Machine Learning Engineer, you will play a critical role in building, scaling, and operating production-grade ML systems that drive real-time personalization across the Attentive platform.\n You will operate with a high degree of ownership, partner closely with Product and Engineering, and help raise the technical bar across our ML systems in a fast-paced, high-impact environment.\n What You’ll Accomplish \n \n You have a proven track record of building systems that maintain a high bar of quality\n You deeply loathe regressions and take proactive steps to protect against them through a variety of testing techniques\n You are a collaborator, technical leader, and a great communicator\n You are constantly improving the quality of the project you are working on, both via direct contributions as well as long-term advocacy for larger-scale changes\n You are enthusiastic about the high impact, fast-paced work environment of an late-stage startup\n 10+ years experience is ideal\n \n Your Expertise \n \n You have worked professionally building systems for 6+ years with experience on a single system long enough to see the consequences of your decisions\n Experience with TensorFlow/Pytorch, xgboost, pandas, matplotlib, SQL, Spark or similar tools\n You have proficiency or experience with Python\n You have extensive experience using machine learning and data analysis, or similar, to build scalable systems and data-driven products, working with cross-functional teams\n You have a proven track record of building scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation from modern research\n You have led cross-functional machine learning projects across teams\n \n What We Use \n \n Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS\n Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm\n Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS\n Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright\n Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas\n \n You'll get competitive  perks and benefits , from health \u0026 wellness to equity, to help you bring your best self to work.\n For US based applicants: \n \n The US base salary range for this full-time position is $320,000 - $360,000 annually   + equity + benefits\n Our salary ranges are determined by role, level and location\n \n #LI-EF1 \n By applying for this position, your data will be processed as per Attentive's Privacy Policy . \n Attentive Company Values \n Default to Action - Move swiftly and with purpose\n Be One Unstoppable Team - Rally as each other’s champions\n Champion the Customer - Our success is defined by our customers' success\n Act Like an Owner  - Take responsibility for Attentive’s success\n  \n Learn more about AWAKE , Attentive’s collective of employee resource groups.\n  \n If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches your skills and experience.\n  \n At Attentive, we know that our Company's strength lies in the diversity of our employees. Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. Our policy is to provide eq","salary_min":320000,"salary_max":360000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["microservices","pytorch","tensorflow","api-design","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/attentive/jobs/4413218009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-21T17:40:02Z","expires_at":"2026-11-13T17:03:24.691684Z","created_at":"2026-10-14T15:25:28.233821Z","updated_at":"2026-10-14T17:03:24.694386Z","company_name":"Attentive","company_slug":"attentive","company_logo_url":"https://www.google.com/s2/favicons?domain=attentive.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1ae30773-57b7-45fa-826d-8ee7eb1cb372"},{"id":"0b3213fe-7b3c-4d47-abb6-e69cbe6f1bad","company_id":"5fac52d7-9b0b-4990-80a2-e2949dd0af1d","title":"Principal Software Engineer, Machine Learning","slug":"principal-software-engineer-machine-learning-489c592f","description":"Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.\n  \n Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.\n  \n With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in  Deloitte’s Fast 500  (four years running!),  LinkedIn’s Top Startups ,  Forbes’ Cloud 100 (five years running!),  Inc.’s Best Workplaces , and the  Human Rights Campaign Foundation's Corporate Equality Index !\n About the Role \n Our Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. As a Senior Machine Learning Engineer, you will play a critical role in building, scaling, and operating production-grade ML systems that drive real-time personalization across the Attentive platform.\n You will operate with a high degree of ownership, partner closely with Product and Engineering, and help raise the technical bar across our ML systems in a fast-paced, high-impact environment.\n What You’ll Accomplish \n \n You have a proven track record of building systems that maintain a high bar of quality\n You deeply loathe regressions and take proactive steps to protect against them through a variety of testing techniques\n You are a collaborator, technical leader, and a great communicator\n You are constantly improving the quality of the project you are working on, both via direct contributions as well as long-term advocacy for larger-scale changes\n You are enthusiastic about the high impact, fast-paced work environment of an late-stage startup\n 10+ years experience is ideal\n \n Your Expertise \n \n You have worked professionally building systems for 10+ years with experience on a single system long enough to see the consequences of your decisions\n Experience with TensorFlow/Pytorch, xgboost, pandas, matplotlib, SQL, Spark or similar tools\n You have proficiency or experience with Python\n You have extensive experience using machine learning and data analysis, or similar, to build scalable systems and data-driven products, working with cross-functional teams\n You have a proven track record of building scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation from modern research\n You have led cross-functional machine learning projects across teams\n \n What We Use \n \n Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS\n Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm\n Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS\n Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright\n Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas\n \n You'll get competitive  perks and benefits , from health \u0026 wellness to equity, to help you bring your best self to work.\n For US based applicants: \n \n The US base salary range for this full-time position is $350,000 - $430,000 annually   + equity + benefits\n Our salary ranges are determined by role, level and location\n \n #LI-EZ-1  \n By applying for this position, your data will be processed as per Attentive's Privacy Policy . \n Attentive Company Values \n Default to Action - Move swiftly and with purpose\n Be One Unstoppable Team - Rally as each other’s champions\n Champion the Customer - Our success is defined by our customers' success\n Act Like an Owner  - Take responsibility for Attentive’s success\n  \n Learn more about AWAKE , Attentive’s collective of employee resource groups.\n  \n If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches your skills and experience.\n  \n At Attentive, we know that our Company's strength lies in the diversity of our employees. Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. 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We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.\n  \n Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.\n  \n With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in  Deloitte’s Fast 500  (four years running!),  LinkedIn’s Top Startups ,  Forbes’ Cloud 100 (five years running!),  Inc.’s Best Workplaces , and the  Human Rights Campaign Foundation's Corporate Equality Index !\n Our Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. 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No job description is perfect, and we may also have another opportunity that closely matches your skills and experience.\n  \n At Attentive, we know that our Company's strength lies in the diversity of our employees. Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. 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