{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"19f2bcb8-dfcf-45a4-b0a7-855bfd707cfa","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Engineering Manager","slug":"engineering-manager-3cdfd46c","description":"Overview\n\nInferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.\n\nAbout the Role\n\nWe're looking for an Engineering Manager to help scale a world-class, senior-heavy engineering organization working at the frontier of AI inference. You will lead a team of exceptional engineers across areas such as inference systems, performance, kernels, accelerators, and cloud infrastructure while creating the management capacity required for Inferact to keep growing quickly without sacrificing technical quality or execution speed.\n\nThis is a deeply technical management role. You will own team execution, hiring, coaching, prioritization, and operating rhythm, while staying close enough to the work to earn the trust of engineers solving hard systems problems. You will partner closely with engineering and company leadership to turn company priorities into clear technical direction, remove blockers, improve how the team works, and help every engineer do the highest-leverage work of their career.\n\n\n\nSkills and Qualifications\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, or similar.\n\n - Demonstrated experience managing strong software engineers in technically demanding systems, infrastructure, ML systems, distributed systems, or performance-oriented environments.\n\n - Strong technical credibility with the ability to understand architecture, ask useful questions, challenge assumptions, and help unblock senior engineers without becoming a bottleneck or overriding domain experts.\n\n - Experience owning the full people-management lifecycle, including hiring, onboarding, 1:1s, feedback, performance management, career development, team health, and retention.\n\n - Ability to translate ambiguous company priorities into clear team goals, sequencing, ownership, and execution plans while maintaining urgency in a fast-changing startup environment.\n\n - Track record of building effective engineering operating rhythms around planning, decision-making, technical reviews, cross-team coordination, and accountability without creating unnecessary process.\n\n - Strong judgment about when to dive into a technical problem, when to delegate, and how to create leverage for senior engineers rather than competing with them for ownership.\n\n - 2+ years of management experience with 10+ reports.\n\nPreferred qualifications:\n\n - Experience leading teams working on AI / ML infrastructure, model serving, distributed systems, GPU infrastructure, compilers, performance engineering, cloud infrastructure, or developer platforms.\n\n - Experience managing senior, staff, principal, research-adjacent, or PhD engineers in an environment where technical credibility and autonomy are especially important.\n\n - Experience scaling an engineering team through rapid hiring, including interviewer calibration, hiring decisions, onboarding systems, and thoughtful team design.\n\n - Familiarity with inference systems and adjacent technologies such as vLLM, SGLang, PyTorch, CUDA / Triton, Kubernetes, distributed serving, or accelerator infrastructure.\n\n - Experience partnering closely with founders or engineering executives in an early-stage company where priorities, team boundaries, and organizational needs evolve quickly.\n\nBonus points if you have:\n\n - Built or scaled a small, unusually high-talent engineering team where the technical surface area was specialized and the quality bar was extremely high.\n\n - Previously remained hands-on as an engineer or technical lead before moving into management and can still reason credibly about complex production systems.\n\n - Managed teams spanning multiple technical specialties, geographies, or time zones while preserving strong ownership and communication.\n\n - Helped establish engineering management practices from an early stage rather than inheriting a mature management system.\n\n - Contributed to open-source infrastructure, AI systems, developer tools, or technically ambitious products used by external engineers.\n\nLogistics\n\n - Location: This role is based in San Francisco, California and is in-office.\n\n - Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\n\n - Visa sponsorship: We sponsor visas on a case-by-case basis.\n\n - Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["cloud","llm","pytorch","gpu","mlops","distributed-systems","research"],"apply_url":"https://jobs.ashbyhq.com/inferact/98837c38-33a9-4085-97e7-1974dcf234a1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T11:23:31.216Z","expires_at":"2026-11-13T16:58:50.529662Z","created_at":"2026-10-14T15:22:31.292734Z","updated_at":"2026-10-14T16:58:50.533097Z","company_name":"Inferact","company_slug":"inferact","company_logo_url":"https://www.google.com/s2/favicons?domain=inferact.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/19f2bcb8-dfcf-45a4-b0a7-855bfd707cfa"},{"id":"fcb2f68d-9ea2-439d-9d09-29431b4bdeec","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Machine Learning Engineer - Gen SWE","slug":"machine-learning-engineer-gen-swe-049769f2","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 This role follows a hybrid work schedule and reports to a Software Tech Lead Manager.\n You will: \n \n \n Own and drive the roadmap for ML-powered pickup and dropoff features, from problem definition through launch, in partnership with PM, UX, data science, and other commercialization engineering teams\n \n Train, evaluate, and deploy ML models for occupancy detection, rider-vehicle interactions, pullover selection, and lost and found\n \n Design fallback systems that cascade between onboard and offboard models based on latency, confidence, and connectivity\n \n Build distributed systems that span vehicles, cloud services, and rider apps, and that handle intermittent and inconsistent connectivity well\n \n Work across the stack (data pipelines, model serving, backend services, and client integration) to ship end-to-end, user-facing features\n \n Define success metrics and use offline evaluation, simulation, and live experiments to measure impact on service quality and rider experience\n \n You have: \n \n \n BS degree in Computer Science or equivalent practical experience\n \n 5+ years of software engineering experience, including building and deploying ML models into production\n \n Experience building backend distributed systems\n \n We prefer: \n \n \n Experience with C++\n \n MS in Computer Science, or equivalent practical experience\n \n Experience in developing machine learning models, infrastructure, and pipelines\n \n Experience deploying models on-device or on edge hardware with compute and latency constraints\n \n Track record of owning ambiguous, user-facing problems end-to-end and setting your own technical direction\n \n Experience in autonomous vehicles or robotics\n \n Disclosure for WA Based \u0026 Remote Roles: \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:\n Health, dental, vision, life, disability insuranceRetirement Benefits: 401(k) with company matchPaid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employmentSick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks Baby Bonding Leave: 18 weeks Holidays: 13 paid days per year\n US Remote Only: Please note that Waymo may not be able to employ remotely in all US locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process.\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":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["mlops","distributed-systems","data-pipeline","autonomous-vehicles","robotics","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8212440","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T13:20:32Z","expires_at":"2026-11-13T16:55:54.938681Z","created_at":"2026-10-14T15:20:04.385328Z","updated_at":"2026-10-14T16:55:54.941384Z","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/fcb2f68d-9ea2-439d-9d09-29431b4bdeec"},{"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":"84dcc4a4-8758-4ae4-a08c-63d080d8c66e","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Principal Machine Learning Platform Engineer, Foundation AI ","slug":"principal-machine-learning-platform-engineer-foundation-ai-dcf9ae0b","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n ML Platform powers hundreds of use cases and billions of inferences per day across Discovery, Safety, Economy, and Creation. We build the primitives that let teams train, evaluate, deploy, and operate models quickly and safely—so a new ML idea can reach production in weeks or less. We’re looking for a Principal Platform Engineer who treats platform as a product : someone who can turn complex ML/AI infrastructure into clear, durable APIs, SDKs and easy-to-use CLIs/UIs that our internal developers love.\n This role blends product thinking, developer experience, backend engineering, and infrastructure at scale. Hands‑on ML experience is a plus; a track record of building internal platforms that developers love is a must.\n You Are \n \n Have 10+ years of professional experience and have a wealth of system design experience upon which to draw to build a scalable, reliable ML platform for all of Roblox.\n A Code Machine; you love not only to design and communicate ideas but also to actually ship product. You obsess about user feedback, and constantly drive towards getting platform features in customers hands.\n Proficient in API design and developer experience—gRPC/REST APIs, SDKs, CLIs, and simple UIs that developers love to use. \n Good to have - experience with the end‑to‑end ML model lifecycle such as model serving, training, model CI/CD, and GPU resources management, and have built ML platform features that are delightful to use. \n You're passionate about infrastructure‑as‑code and automating painful manual processes. You push for platform solutions that don’t leave on-call teams carrying the burden of design choices.\n Passionate about supporting ML engineers to meet and understand their needs, and translating them into clean, durable platform abstractions.\n A clear communicator who excels at both written and verbal communication across differing levels of technical detail. \n Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a similar technical field.\n \n You Will \n \n Own platform as a product and set direction end to end: Define requirements, write RFDs, and ship APIs, SDKs, CLIs, and UIs that make ML@Roblox easy to adopt.\n Bootstrap and maintain core ML Platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes.\n Set technical strategy and oversee development of high scale and reliable infrastructure systems, with clear SLOs for latency, availability, and cost. \n Design great developer experiences with paved‑road templates, golden paths, opinionated defaults, and clear docs to reduce time‑to‑first‑production. \n Instrument the platform to measure adoption, friction, reliability, and cost; use data to prioritize roadmap and validate outcomes. \n Partner across organizations (ML Engineering, Data Science, Infra/SRE, Security, Finance) to optimize performance, safety, and spend, especially for GPU‑intensive training and high‑QPS inference. \n Propose and implement new platform tooling to improve time to production for MLEs across the full ML lifecycle.\n Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading‑edge technologies and practices. \n Mentor junior and senior engineers, lead design reviews, and drive cross‑team architectural decisions that last.\n For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page .\n Annual Salary Range\n $295,250 — $345,050 USD \n Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).\n Roblox provides equal employment opportunities to all e","salary_min":295250,"salary_max":345050,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["mlops","api-design","platform","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/8233722?gh_jid=8233722","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T20:57:31Z","expires_at":"2026-11-13T17:02:53.15447Z","created_at":"2026-10-14T15:24:56.150214Z","updated_at":"2026-10-14T17:02:53.157398Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/84dcc4a4-8758-4ae4-a08c-63d080d8c66e"},{"id":"32e747ed-8b79-450b-9035-c6baf08039fa","company_id":"82d2abc2-444c-4d89-9646-4739e72d700d","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-2cccd4cc","description":"About Checkr Checkr is building the data platform to power safe and fair decisions. Over 140,000 companies and millions of people rely on Checkr for AI verification in the moments that matter most: getting a new job, a new place to live, a car ride, childcare, even a date. Customers include Uber, Pennymac, Airbnb, Doordash, and Anthropic. We’re a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on Forbes Cloud 100 2025 List and is a Y Combinator 2024 Breakthrough Company .\n About the team/role \n We’re hiring an ML Engineer (P3) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data \u0026 ML organization within Engineering.\n Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.\n This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.\n This role sits in the central Data \u0026 ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.\n We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.\n What you’ll do \n \n Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.\n Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.\n Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.\n Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.\n Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.\n Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.\n \n  \n What you bring \n \n A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience\n 6+ years building software professionally, with at least 2 of those building ML systems that run in production\n Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation\n You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on\n Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal\n You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it\n An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes. \n \n Nice to have \n \n Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar)\n Background in document processing, OCR, or information extraction\n Experience with PySpark or large-scale data processing\n Ruby experience (Checkr’s platform runs on Rails)\n Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)\n Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools\n Pay Transparen","salary_min":176000,"salary_max":207000,"location":"Denver, CO","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","code-generation","payments","legal","llm","mlops","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/checkr/jobs/8274246","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T16:04:33Z","expires_at":"2026-11-13T16:58:42.655818Z","created_at":"2026-10-14T15:22:20.04347Z","updated_at":"2026-10-14T16:58:42.658798Z","company_name":"Checkr","company_slug":"checkr","company_logo_url":"https://www.google.com/s2/favicons?domain=checkr.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/32e747ed-8b79-450b-9035-c6baf08039fa"},{"id":"30f0bfd5-b030-48f6-9124-0c1234f28ba7","company_id":"31ae48bc-c938-4c26-a348-0bf3c089a446","title":"Staff Product Manager, AI Inference","slug":"staff-product-manager-ai-inference-85275e80","description":"CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com . \n About the Team \n As the Staff Product Manager - AI Inference, you will lead CoreWeave's strategic efforts in shaping and developing cutting-edge AI inference services that drive innovation and business outcomes for our customers. You will collaborate closely with engineers, designers, and industry partners to create world-class products that empower organizations to build, deploy, scale, and optimize AI models and applications efficiently and effectively.\n The ideal candidate is passionate about AI and cloud technologies, with a deep understanding of the foundation model architectures, AI inference landscape, AI application ecosystem, cloud-native technologies, and the competitive landscape. You will leverage market insights and technical expertise to define the product vision, roadmap, and go-to-market strategy, ensuring that CoreWeave builds on its market-segment position as the leading AI Hyperscaler.\n Key Responsibilities: \n \n Product Vision and Strategy: Help refine the product strategy for AI application cloud services aimed at foundational model Inference, along with focus on optimization across the entire CoreWeave product portfolio for AI workloads. Solve novel problems related to scaling AI model inference on specialized hardware.\n Roadmap Development: Own the product roadmap for AI inference services, balancing near-term customer needs with long-term vision, to deliver differentiated and scalable AI cloud services.\n AI and Cloud Expertise: Stay current on advancements in AI/ML technologies including inference engines, model serving architectures, cloud infrastructures, and software development methodologies to inform product decisions and foster innovation.\n Define product portfolio: Manage ambiguity and complexity of a fast evolving AI landscape to define new product investments and develop a portfolio of inference capabilities that deliver long-term differentiation and customer value.\n Customer Engagement: Collaborate with key customers to understand their AI workloads, pain points, and future requirements to continuously improve our service offerings.\n Cross-functional Leadership: Collaborate with engineering, sales, marketing, and customer success teams to ensure seamless product development and deployment.\n Market Analysis: Analyze market trends, customer feedback, and the competitive landscape to identify growth opportunities and ensure CoreWeave stays ahead of industry trends.\n Go-to-Market Strategy: Partner with industry partners, marketing, and sales teams to develop and execute effective go-to-market strategies for AI cloud services, including pricing strategies, positioning, messaging, and competitive differentiation.\n \n Qualifications: \n \n Experience: 8+ years of experience in product management, with at least 5 years in the cloud services or AI/ML market segment.\n Technical Knowledge: Hands on practitioner and deep understanding of AI/ML workloads including foundation model inference, inference engines such as vllm, SGLang, TensorRT-llm, and llm-d, inference optimization techniques such as quantization and speculative decoding, cloud infrastructure (GPUs), and cloud-native architectures (Kubernetes, microservices).\n Leadership: Proven ability to lead cross functional teams, with strong decision-making, project management, and mentoring skills.\n Customer-Centric Approach: Demonstrated experience working closely with customers to identify needs and translate them into product features and solutions.\n Communication Skills: Excellent verbal and written communication skills with the ability to influence cross-functional teams and stakeholders.\n Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field. MBA or equivalent experience is a plus.\n Visionary Leadership: Ability to think strategically while being able to dive into technical details to guide the product team.\n Results-Oriented: Proven track record of driving business growth through innovative product solutions in the AI and cloud space.\n \n Why CoreWeave? \n At CoreWeave, we work hard, have fun, and move fast! We're in an exciting stage of hyper-growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:\n \n Be Curious at Your Core\n Act Like an Owner\n Empower Employees\n Deliver Best-in","salary_min":207000,"salary_max":275000,"location":"Sunnyvale, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["microservices","mlops","cloud","generative-ai","llm","inference"],"apply_url":"https://coreweave.com/careers/job?4720733006\u0026board=coreweave\u0026gh_jid=4720733006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T14:43:58Z","expires_at":"2026-11-13T16:56:02.427193Z","created_at":"2026-10-14T15:20:12.017713Z","updated_at":"2026-10-14T16:56:02.430397Z","company_name":"CoreWeave","company_slug":"coreweave","company_logo_url":"https://www.google.com/s2/favicons?domain=coreweave.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/30f0bfd5-b030-48f6-9124-0c1234f28ba7"},{"id":"7a65d567-bcff-488f-a593-6e31f5da1a79","company_id":"e939609d-98b9-49ee-9737-a7134cd10d11","title":"Deployment Engineer","slug":"deployment-engineer-f8b717e9","description":"About Comet \n Comet is building the development platform for teams who want to ship robust, reliable, and responsible AI applications. Opik, our open source LLM evaluation framework, has quickly become one of the most popular tools in the space. Our experiment management platform is used by data scientists at companies like Uber, Netflix, and Etsy. Tens of thousands of researchers, engineers, academics, and hobbyists use Comet every day to build the future of AI. \n Working at Comet will give you access to the most exciting work being done in all areas of machine learning. Some of the top researchers and companies working on self-driving cars, drug discovery, particle research, diffusion models, and LLMs use Comet every day. Your work has the potential to accelerate the development of some of the most impactful technology in the world, and you will be doing it alongside a team of passionate, caring individuals. If that sounds exciting, Comet is the right place for you.\n Comet is backed by more than $63 million in venture capital funding and powers some of the best machine-learning teams in the world, including Netflix, Uber, Etsy, and Mobileye. We are a remote-first company with offices in New York City (USA) and Tel Aviv (Israel).\n About the Role: \n Comet is looking for an experienced Deployment Engineer (located in the East or Central USA Only) to help our customers successfully deploy, operate, and troubleshoot Comet in their environments.\n This is a highly customer-facing engineering role . You will work directly with customers to understand their infrastructure and deployment requirements, lead deployments, troubleshoot complex issues, and drive problems through to resolution.\n You will also contribute to the engineering and automation behind our deployment solutions, including Kubernetes, Helm, Terraform, cloud infrastructure, tooling, and documentation.\n What You’ll Do: \n \n Work directly with customers to understand their infrastructure and deployment requirements.\n Deploy and maintain Comet across Kubernetes and cloud environments.\n Troubleshoot complex infrastructure and deployment issues across Kubernetes, networking, cloud services, databases, and application configuration.\n Own customer issues from investigation through resolution, collaborating with Engineering, Support, Customer Success, and other teams when needed.\n Develop and improve Helm charts, Terraform, deployment tooling, automation, and documentation.\n Participate in technical customer calls and explain complex topics clearly.\n Use AI tools for troubleshooting, development, automation, and investigation while validating results and maintaining engineering judgment.\n \n What We’re Looking For: \n \n Proven customer-facing technical experience is essential. \n Proven experience using AI tools in real engineering workflows, with the ability to critically evaluate and validate their output.\n Strong hands-on experience with  Kubernetes, Helm and containerized applications.\n Production experience with cloud infrastructure, preferably  AWS .\n Experience with  Infrastructure as Code , preferably Terraform.\n Strong Linux, networking, and infrastructure troubleshooting skills.\n Experience troubleshooting production systems using logs, metrics, and monitoring/observability tools.\n Excellent written and verbal communication skills.\n \n How you work:\n \n You take ownership and proactively move problems forward.\n You are passionate about troubleshooting unfamiliar systems and looking for root causes rather than quick workarounds.\n You can work independently while knowing when to collaborate or ask for help.\n You communicate progress, blockers, and decisions clearly - especially in a remote environment.\n You are comfortable with ambiguity and changing priorities.\n You work well across teams and care about both the customer experience and engineering quality.\n \n Nice to Have: \n \n Enterprise or self-hosted software experience.\n Multi-cloud or on-premises deployment experience.\n CI/CD and GitOps experience.\n MLOps or AI infrastructure experience.\n Software development experience.\n Startup experience.\n \n What We Offer: \n \n Competitive base salary - $150K-200K based on proven experience, skills and location.\n Competitive benefits package.\n Flexible working hours and remote work options.\n Opportunities for professional growth and development.\n A collaborative and innovative work environment.\n The chance to work with cutting-edge technologies and projects.\n This role will be fully remote , located in the East or Central USA Only , working with a global team (large presence in the US, Europe and Tel Aviv) – some flexibility with work hours is required.\n \n  \n Please note: We do not accept unsolicited resumes or candidate submissions from recruitment agencies for this position. \n  \n  \n Comet is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees without regard to race, religion, ","salary_min":150000,"salary_max":200000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"mid","tags":["mlops","llm","diffusion-models","cloud","autonomous-vehicles"],"apply_url":"https://www.comet.com/site/about-us/careers/job/?gh_jid=8017201003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T21:02:46Z","expires_at":"2026-11-13T16:59:35.153106Z","created_at":"2026-10-14T15:23:07.034809Z","updated_at":"2026-10-14T16:59:35.156267Z","company_name":"Comet ML","company_slug":"comet-ml","company_logo_url":"https://www.google.com/s2/favicons?domain=comet.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7a65d567-bcff-488f-a593-6e31f5da1a79"},{"id":"208f6a9b-9968-46f0-aa68-f972d885cce0","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Senior Data Scientist, Systems Performance","slug":"senior-data-scientist-systems-performance-fc07da4b","description":"Mission Summary: \n The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas.\n Rigorous behavioral and system performance evaluation is critical to scaling our service and achieving Motional's long-term goals. We are seeking a Senior Data Scientist to lead initiatives that improve evaluation and testing methodologies, measure the quality and trustworthiness of our evaluation portfolio, and partner with engineering teams to monitor and strengthen the health of the evaluation ecosystem. You will help ensure Motional's performance evaluation is efficient, scientifically rigorous, and aligned with our growth priorities.\n In this role, you will lead development of evaluation methodologies and metrics that assess the quality and business relevance of solutions spanning on-road and off-board data. You will influence the evaluation signals software engineers rely on to validate that changes to the autonomy stack deliver intended improvements, conduct deep-dive analyses to understand bottlenecks in current methodologies, and prototype improvements in metrics, sampling strategy, and statistical inference. You will develop deep expertise in how evaluation signals inform launch and release decisions, weigh trade-offs across the evaluation portfolio, and provide actionable insights for designing launch criteria.\n If you are a rigorous, collaborative data scientist with a passion for improving how autonomous systems are measured and validated at scale, we encourage you to apply. \n What You’ll Be Doing: \n \n Lead the development of evaluation frameworks for the autonomous system, connecting technical problems to rigorous, data-driven approaches for measuring and validating performance. \n Collaborate closely with Functional Safety and Systems Engineering teams to ensure evaluation metrics map effectively to automotive safety standards (e.g., SOTIF, ISO 21448) and launch readiness decisions.\n Ensure evaluation metrics are reliable enough to inform safety cases and launch readiness decisions.\n Monitor the reliability of evaluation metrics and incoming performance data over time, including detecting drift, inconsistencies, and degradation in metric definitions, to ensure the evaluation ecosystem remains accurate and trustworthy.\n Drive our approach to performance analysis using data-backed statistical methods for simulation and on-road data.\n Develop new statistical analysis methods to analyze AV performance data and lead by example in applying them to real problems.\n Partner with triage operators and simulation engineers to turn raw disengagements and identified edge cases into procedural or generative scenarios, feeding them back into the simulation catalog to strengthen test coverage.\n Use fleet and evaluation data to identify edge cases in an automated manner and coverage gaps, and partner with engineering to feed novel scenarios back into the simulation catalog and strengthen test coverage.\n Build confidence in the evaluation framework through data-driven insights and clear communication of findings to technical leaders and stakeholders.\n Establish correlation between on-road and simulation data to improve how we interpret and act on evaluation results.\n Make sense of large datasets to drive insights, solve ambiguous performance questions, and communicate results effectively across teams and upward to leadership.\n Establish a self-service model for developers to understand the impact of their changes.\n Develop new metrics, interpret trends, and investigate anomalies in simulation and on-road data. \n Collaborate with developers to drive action based on these results.\n Serve as an advisor and influence collaborators across multiple teams, promote data-aware decision making, and establish best practices around the use of data.\n Mentor and collaborate with fellow engineers and foster a positive, collaborative work environment.\n Introduce the use of ML methods for performance evaluation where they add rigor and scale. \n \n What You Bring: \n \n 5+ years of industry experience solving complex problems with large datasets, with a track record of framing ambiguous questions into rigorous, data-driven analyses.\n Bachelor's or higher degree in Computer Science, Computer Engineering, Data Science, Robotics, Physics, Mathematics, or a related quantitative field. Master's or PhD preferred.\n Strong problem-solving skills: ability to break down complex performance and evaluation challenges, think logically, and remove bias from how problems are defined and assessed.\n Strong Python and SQL skills, with demonstrated expe","salary_min":149000,"salary_max":198500,"location":"Boston, MA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","robotics","mlops","deep-learning","data-pipeline","data-science"],"apply_url":"https://motional.com/open-positions/?gh_jid=8016984003#/8016984003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T17:27:51Z","expires_at":"2026-11-13T16:56:23.376677Z","created_at":"2026-10-14T15:20:30.457065Z","updated_at":"2026-10-14T16:56:23.379931Z","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/208f6a9b-9968-46f0-aa68-f972d885cce0"},{"id":"77f59048-ecf1-449b-8a20-a053e12e7b32","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Staff AI \u0026 Agentic Platform","slug":"staff-ai-agentic-platform-404811aa","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 Discovery is Anduril's team for taking the newest problems across domains- space, missile systems, air, sensor capability, autonomy, and cyber- and proving out what is worth solving. We build the models, run the tests, and carry what works to the point where a program can pick it up. Discovery works alongside Air Defense, Space, Intelligence, Cyber, GNC, Hardware, and every other group at Anduril to incubate the solutions to the hardest problems. \n ABOUT THE JOB \n Discovery is building an expeditionary force: a team of engineers who want to solve difficult problems and who navigate unfamiliar territory as an operating standard. As a Discovery engineer, you pick up a concept nobody has proven and build the analysis or prototype that tests it to get to an answer. Whether you spend your time working on hard problems on our autonomy stack, acoustic sensor analysis, or battle space management, we tackle every challenge the same way: by questioning assumptions and building our way to the answer.\n We are focused on taking AI models from research into production—onto classified platforms and edge hardware where they have to perform reliably in the real world. As our model portfolio and classified work grow, rigorous, repeatable evaluation of how these models actually perform has become mission-critical.\n WHAT YOU'LL DO \n \n Develop Test Scenarios Simulation Environments: Develop comprehensive test scenarios, and simulation environments to assess agentic AI performance in classified simulations.\n Validate AI on Classified \u0026 Edge Platforms: Lead the integration and validation of agentic AI systems onto classified platforms and edge hardware.\n Build Reusable Evaluation Pipelines: Build reusable evaluation pipelines, automated test harnesses, and monitoring dashboards for continuous validation.\n Define Actionable Metrics: Figure out what good metrics look like for AI models and traditional models—establishing systematic, historic capture of performance rather than one-shot, deployment-specific measurement.\n Partner Cross-Functionally: Partner with cross-functional teams to define requirements, document test results, and contribute to AI model cards and deployment readiness reviews.\n Own End-to-End Evaluation: Architect, build, and maintain the evaluation and validation infrastructure for our agentic AI and ML models, from unit-level model checks through full-system, scenario-based assessment.\n Troubleshoot and Debug: Analyze and resolve issues uncovered in evaluation and in deployment, ensuring reliability and operational success across every release.\n \n REQUIRED QUALIFICATIONS \n \n Technical Expertise: Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, Computer Engineering, Electrical Engineering, or a related technical field.\n Programming Proficiency: At least 8-10 years of hands-on experience writing production-grade code, with strong Python skills for building evaluation pipelines, test harnesses, and tooling.\n Evaluation Ownership Mindset: Demonstrated experience owning evaluation or automated testing for production ML or software systems—test scenarios, metrics, regression suites, and CI infrastructure.\n AI/ML Evaluation Experience: Hands-on experience designing evaluation methodologies for AI or machine learning models—defining metrics, building benchmarks, and assessing model behavior against real-world scenarios.\n Simulation Experience: Experience designing or working extensively with simulation environments to exercise model and system behavior.\n Metrics Discipline: Working knowledge of how to define, capture, and reason about performance metrics for AI models and traditional models, including systematic historic capture over time.\n Systems-Level Thinking: Ability to navigate and contribute to complex systems and established codebases.\n Program Ownership: Comfort operating between technical program management and software engineering—defining requirements, coordinating across teams, and documenting results.\n Real-World Impact: Passion for building the evaluation infrastructure that proves AI works—directly influencing mission-critical outcomes.\n Security Clearance: Must have a TS/SCI clearance.\n \n PREFERRED QUALIFICATIONS \n","salary_min":253000,"salary_max":336000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["computer-vision","mlops","agents","payments","cloud"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5261106007?gh_jid=5261106007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T15:28:13Z","expires_at":"2026-11-13T16:57:16.637678Z","created_at":"2026-10-14T15:21:01.892591Z","updated_at":"2026-10-14T16:57:16.640761Z","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/77f59048-ecf1-449b-8a20-a053e12e7b32"},{"id":"89d58d57-03e8-4684-83b2-3121d619b65f","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, ML Serving Platform","slug":"software-engineer-ml-serving-platform-c430ad40","description":"About the Team \n DoorDash’s ML Serving Platform delivers tens of millions of predictions per second , powering search, recommendations, advertising, delivery estimates, and logistics across DoorDash, Wolt, and Deliveroo.\n Our customers are modelers and engineering teams across our internal business verticals. We build self-serve infrastructure that empowers them to bring new models into production, adopt open source software and models, and expand what they can accomplish with machine learning at scale.\n We work at the intersection of a rapidly evolving open source ecosystem and demanding production workloads. With active customer demand and growing modeling ambitions, we’re advancing the infrastructure behind today’s predictions while building the capabilities that enable the next generation of ML innovation.\n About the Role \n You’ll help build the next generation of our ML serving platform, connecting request routing and online feature retrieval with model inference on CPU and GPU infrastructure. You’ll tackle challenging infrastructure problems involving latency, reliability, resource efficiency, and scale, and make those capabilities accessible through self-serve tools and workflows.\n Working alongside experienced platform engineers, you’ll own defined projects from technical design and implementation through testing, rollout, and production support. You’ll partner directly with internal teams to understand emerging modeling requirements, remove adoption barriers, and turn advances in open source technology into measurable production improvements.\n You’re excited about this opportunity because you will… \n \n Empower modelers through self-serve infrastructure. Build tools, APIs, and workflows that help internal teams deploy, configure, validate, and operate models independently, accelerating ML adoption across business verticals.\n Bring open source innovation into production. Evaluate and integrate evolving inference frameworks and enable open source models, translating promising capabilities into reliable, efficient services at scale.\n Solve demanding inference infrastructure problems. Improve the systems that route prediction requests, retrieve online features, and execute models on a platform serving tens of millions of predictions per second.\n Help evolve our disaggregated serving architecture. Build modular components that allow routing, feature retrieval, and model execution to evolve and scale independently as workloads and modeling requirements change.\n Improve Kubernetes deployment and autoscaling. Help workloads respond to changing traffic while meeting latency and availability requirements and using CPU and GPU resources efficiently.\n Make adoption and rollout easier. Build integrations, validation, and migration tooling that help teams adopt new serving capabilities and our unified global platform with confidence.\n Own performance and reliability in production. Use benchmarking, profiling, metrics, and tracing to identify bottlenecks; participate in on-call and improve automation and runbooks to make the platform easier to operate.\n \n We’re excited about you because… \n \n You have 2+ years of software engineering experience building and maintaining production services or infrastructure.\n You have strong computer science fundamentals and proficiency in a backend or systems programming language such as Java, Kotlin, Go, C++, or Python.\n You understand distributed systems fundamentals, including concurrency, networking, timeouts, failure handling, and performance trade-offs.\n You enjoy challenging infrastructure problems and can independently turn a defined problem into a technical design, tested implementation, and safe production rollout.\n You have experience debugging production systems and using operational data to improve reliability, performance, or cost.\n You care about the engineers using your platform and collaborate effectively with customers and partners to make complex infrastructure easier to adopt.\n You hold a degree in Computer Science or a related field, or have equivalent practical experience.\n \n Nice to Have \n \n Experience with ML inference infrastructure, online feature retrieval, or other latency-sensitive distributed services.\n Experience operating containerized workloads on Kubernetes, including deployment, resource management, or autoscaling.\n Experience building self-serve developer platforms, APIs, or automation that helps other engineers move faster.\n Experience integrating open source infrastructure or models and validating their behavior under production workloads.\n Familiarity with CPU/GPU performance profiling, inference runtimes, or model deployment workflows.\n \n Applications for this position are accepted on an ongoing basis\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 lo","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["mlops","distributed-systems","fine-tuning","cloud","gpu","healthcare","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8263726","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:36Z","expires_at":"2026-11-13T17:03:28.772861Z","created_at":"2026-10-14T15:25:31.414102Z","updated_at":"2026-10-14T17:03:28.775658Z","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/89d58d57-03e8-4684-83b2-3121d619b65f"},{"id":"64d23aa4-9ec2-400e-a028-2a5364b6e205","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Software Engineer, Machine Learning Platform - Gen AI","slug":"software-engineer-machine-learning-platform-gen-ai-ba861ebf","description":"About the Team \n DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.\n About the Role \n You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll work across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability. This role is ideal for an engineer who enjoys pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly.\n You’re excited about this opportunity because you will… \n \n Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company.\n Work on our open-weights serving stack — real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.\n Design scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases\n Push the cost and latency frontier of GPU inference — turning batch jobs that took days into hours and cutting inference cost by multiples — while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in.\n Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence.\n Partner closely with ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives.\n Shape the future of DoorDash’s centralized GenAI platform — including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimization, and other post-training and agentic techniques — enabling the next generation of AI-powered products, agents, automation, and personalization.\n \n We’re excited about you because… \n \n B.S., M.S., or PhD. in Computer Science or equivalent\n 3+ years of industry experience in software engineering\n Strong backend engineering fundamentals, especially in Python and distributed systems.\n Experience building production services, APIs, data pipelines, or ML infrastructure at scale.\n Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization.\n Hands-on experience with LLM inference and/or fine-tuning of open-weight models in production — serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine-tuning (SFT/DPO/LoRA).\n Ability to work across ambiguous, fast-moving technical areas and turn customer use cases into reusable platform capabilities\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 \n Nice To Haves \n \n Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM) in production\n Experience with distributed/multi-node fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation\n GPU performance work — multi-node/distributed inference, KV-cache/memory optimization, quantization (FP8/INT8/AWQ/GPTQ), or cold-start/throughput tuning\n Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal), or high-throughput batch systems\n Experience with LLM gateways, model routing, vendor abstraction, or cost attribution\n Experience building developer platforms, internal platforms, or self-serve infrastructure\n Experience building and deploying AI a","salary_min":130600,"salary_max":192000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["generative-ai","cloud","embeddings","mlops","fine-tuning","llm","distributed-systems","agents"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8264542","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T14:17:32Z","expires_at":"2026-11-13T17:03:28.762428Z","created_at":"2026-10-14T15:25:31.403168Z","updated_at":"2026-10-14T17:03:28.76533Z","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/64d23aa4-9ec2-400e-a028-2a5364b6e205"},{"id":"bb310930-8502-4a93-ad2a-1918db8f50ca","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Principal Machine Learning Engineer, Applied AI ","slug":"principal-machine-learning-engineer-applied-ai-2ffeead3","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking a Principal Machine Learning Engineer, Applied AI  with deep expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts.\n Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product.\n This is a high-impact senior IC role for someone who brings deep, hands-on expertise in model post-training, evaluation, and production ML systems, built over many years of solving this exact class of problem.\n What You'll Be Building \n \n Apply deep, hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.\n Lead post-training efforts using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.\n Build and run evaluation loops that measure model quality, reliability, and customer fit, using patterns you've refined across many prior projects.\n Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.\n Partner with AI researchers to translate model advances into reliable, usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug complex model failures using traces, evaluations, customer context, and scientific feedback.\n Mentor engineers and share reusable patterns for model adaptation, evaluation, and deployment, drawing on lessons learned across a long track record of shipping ML systems. \n \n What You'll Need to Succeed \n \n Minimum 2-3 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before.\n Strong software engineering skills in Python and modern ML frameworks like PyTorch.\n A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before.\n Experience leading technical work across research and engineering teams.\n Deep familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. \n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Experience training MoE architectures.\n A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $252,000 — $336,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an en","salary_min":252000,"salary_max":336000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["pytorch","llm","reinforcement-learning","mlops","agents","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4400744009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T15:52:55Z","expires_at":"2026-11-13T17:03:03.861561Z","created_at":"2026-10-14T15:25:07.336193Z","updated_at":"2026-10-14T17:03:03.864854Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bb310930-8502-4a93-ad2a-1918db8f50ca"},{"id":"ec0beeba-9413-474e-88c1-ed020950d565","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior / Staff Machine Learning Engineer, Applied AI","slug":"senior-staff-machine-learning-engineer-applied-ai-80cee647","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking a Senior / Staff Machine Learning Engineer, Applied AI with expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts.\n Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product.\n This is the right role for someone who wants to go deep on model adaptation and evaluation, take real ownership of hard technical problems, and grow into broader scope over time. \n What You'll Be Building \n \n Apply hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.\n Run post-training work using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.\n Run evaluation loops that measure model quality, reliability, and customer fit.\n Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.\n Partner with AI researchers to translate model advances into reliable, usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug complex model failures using traces, evaluations, customer context, and scientific feedback.\n \n What You'll Need to Succeed \n \n Minimum 1-2 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before.\n Strong software engineering skills in Python and modern ML frameworks like PyTorch.\n A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before.\n Deep familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. \n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Experience training MoE architectures.\n A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $180,000 — $298,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.\n We’re All In \n Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran s","salary_min":180000,"salary_max":298000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","mlops","agents","llm","reinforcement-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4400741009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T15:52:17Z","expires_at":"2026-11-13T17:03:04.138761Z","created_at":"2026-10-14T15:25:07.633139Z","updated_at":"2026-10-14T17:03:04.141843Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ec0beeba-9413-474e-88c1-ed020950d565"},{"id":"4dcc37f5-6f23-4bac-9d72-ddcbec05893a","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Senior Machine Learning Platform Engineer, Foundation AI","slug":"senior-machine-learning-platform-engineer-foundation-ai-582c933e","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n ML Platform powers hundreds of use cases and billions of inferences per day across Discovery, Safety, Economy, and Creation. We build the primitives that let teams train, evaluate, deploy, and operate models quickly and safely—so a new ML idea can reach production in weeks or less. We’re looking for a Principal Platform Engineer who treats platform as a product : someone who can turn complex ML/AI infrastructure into clear, durable APIs, SDKs and easy-to-use CLIs/UIs that our internal developers love.\n This role blends product thinking, developer experience, backend engineering, and infrastructure at scale. Hands‑on ML experience is a plus; a track record of building internal platforms that developers love is a must.\n You Are \n \n Have 7+ years of professional experience and have a wealth of system design experience upon which to draw to build a scalable, reliable ML platform for all of Roblox.\n A Code Machine; you love not only to design and communicate ideas but also to actually ship product. You obsess about user feedback, and constantly drive towards getting platform features in customers hands.\n Proficient in API design and developer experience—gRPC/REST APIs, SDKs, CLIs, and simple UIs that developers love to use. \n Good to have - experience with the end‑to‑end ML model lifecycle such as model serving, training, model CI/CD, and GPU resources management, and have built ML platform features that are delightful to use. \n You're passionate about infrastructure‑as‑code and automating painful manual processes. You push for platform solutions that don’t leave on-call teams carrying the burden of design choices.\n Passionate about supporting ML engineers to meet and understand their needs, and translating them into clean, durable platform abstractions.\n A clear communicator who excels at both written and verbal communication across differing levels of technical detail. \n Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a similar technical field.\n \n You Will \n \n Own platform as a product and set direction end to end: Define requirements, write RFDs, and ship APIs, SDKs, CLIs, and UIs that make ML@Roblox easy to adopt.\n Bootstrap and maintain core ML Platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes.\n Set technical strategy and oversee development of high scale and reliable infrastructure systems, with clear SLOs for latency, availability, and cost. \n Design great developer experiences with paved‑road templates, golden paths, opinionated defaults, and clear docs to reduce time‑to‑first‑production. \n Instrument the platform to measure adoption, friction, reliability, and cost; use data to prioritize roadmap and validate outcomes. \n Partner across organizations (ML Engineering, Data Science, Infra/SRE, Security, Finance) to optimize performance, safety, and spend, especially for GPU‑intensive training and high‑QPS inference. \n Propose and implement new platform tooling to improve time to production for MLEs across the full ML lifecycle.\n Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading‑edge technologies and practices. \n Mentor junior and senior engineers, lead design reviews, and drive cross‑team architectural decisions that last.\n For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page .\n Annual Salary Range\n $243,290 — $312,970 USD \n Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).\n Roblox provides equal employment opportunities to all em","salary_min":243290,"salary_max":312970,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","api-design","machine-learning","platform"],"apply_url":"https://careers.roblox.com/jobs/8233720?gh_jid=8233720","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T19:14:18Z","expires_at":"2026-11-13T17:02:53.254178Z","created_at":"2026-10-14T15:24:56.251553Z","updated_at":"2026-10-14T17:02:53.257447Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4dcc37f5-6f23-4bac-9d72-ddcbec05893a"},{"id":"65c8e5bf-cd0c-4f57-81c1-58c73a645c23","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Member of Technical Staff, Forward Deployed Engineer","slug":"member-of-technical-staff-forward-deployed-engineer-8df75afb","description":"OVERVIEW\n\nInferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Forward Deployed Engineer to make Inferact successful inside real customer environments. You'll work directly with customers to deploy, integrate, debug, and optimize vLLM-powered inference systems across cloud, Kubernetes, GPU, networking, and model-serving environments.\n\nThis is a hands-on engineering role, not a traditional pre-sales position. You'll move from architecture discussions to implementation, own difficult production problems end-to-end, and work closely with core product and engineering teams to turn what you learn in the field into reusable product capabilities. Your work will directly affect customer time-to-value and how Inferact's platform evolves.\n\n\n\n\nSKILLS AND QUALIFICATIONS\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.\n\n - Strong software engineering ability in Python, Go, TypeScript, or similar, with experience building production-quality integrations, tooling, services, automation, or prototypes.\n\n - Hands-on experience deploying or operating ML systems, model serving, AI infrastructure, cloud platforms, Kubernetes, or high-scale backend systems in production.\n\n - Ability to work directly with sophisticated customer engineering teams, understand ambiguous technical environments, and personally drive implementations and debugging to resolution.\n\n - Strong systems debugging skills across application, runtime, infrastructure, networking, identity, storage, observability, and distributed-system boundaries.\n\n - Ability to reason about latency, throughput, batching, model/runtime compatibility, scaling, reliability, and cost tradeoffs in production inference environments.\n\n - High ownership and strong technical communication, with the judgment to distinguish one-off customer work from problems that should become reusable product capabilities.\n\nPreferred qualifications:\n\n - Experience with vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, BentoML, or other LLM inference and model-serving systems.\n\n - Experience with NVIDIA or AMD GPUs, CUDA / ROCm, GPU scheduling, multi-GPU serving, or accelerator-backed infrastructure.\n\n - Experience deploying infrastructure software into enterprise, regulated, security-sensitive, or bring-your-own-cloud environments.\n\n - Experience building APIs, SDKs, CLIs, developer tooling, deployment platforms, control planes, or infrastructure products used by technical teams.\n\n - Experience profiling latency, throughput, concurrency, GPU utilization, bottlenecks, and performance regressions.\n\nBonus points if you have:\n\n - Contributed to open-source ML systems, inference infrastructure, cloud infrastructure, Kubernetes, or developer tooling.\n\n - Worked in a forward-deployed, customer engineering, field engineering, or highly technical solutions role where you personally wrote and shipped code.\n\n - Built deployment playbooks, reference architectures, automation, or tooling that materially reduced customer time-to-production.\n\n - Resolved severe customer-facing production issues that crossed multiple technical layers and required close partnership with core engineering.\n\n - Turned repeated customer problems into reusable product features, abstractions, documentation, or platform improvements.\n\n\n\n\nLOGISTICS\n\n - Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.\n\n - Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\n\n - Visa sponsorship: We sponsor visas on a case-by-case basis.\n\n - Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["gpu","llm","mlops","cloud","research"],"apply_url":"https://jobs.ashbyhq.com/inferact/b127864f-5c71-4c96-90ce-cb20c57391a9/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-02T16:26:16.571Z","expires_at":"2026-11-13T16:58:50.520451Z","created_at":"2026-10-14T15:22:31.282956Z","updated_at":"2026-10-14T16:58:50.523264Z","company_name":"Inferact","company_slug":"inferact","company_logo_url":"https://www.google.com/s2/favicons?domain=inferact.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/65c8e5bf-cd0c-4f57-81c1-58c73a645c23"},{"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":"e8dcc33f-fb7f-4adc-ba9d-72d06ffc5f5f","company_id":"9bad7e3a-74e6-4dae-87c5-f3e9f0e72bd0","title":"Perception Deployment Engineer - Model Deployment \u0026 Optimization","slug":"perception-deployment-engineer-model-deployment-optimization-1c7dcbba","description":"The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence.\nAs a Perception Deployment Engineer, you will focus on bringing highly efficient, production-ready large-scale models to our on-vehicle stack. We are looking for experts with hands-on experience in compressing, accelerating, and deploying complex computer vision or foundation models for power- and thermal-constrained vehicle SOCs. You will optimize the ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices.\n","salary_min":199000,"salary_max":270000,"location":"Foster City, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","gpu","generative-ai","computer-vision"],"apply_url":"https://jobs.lever.co/zoox/f2da6b2e-791a-49b1-87e6-d0313b105507/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T21:31:00.469Z","expires_at":"2026-11-13T16:56:21.282396Z","created_at":"2026-10-14T15:20:28.457872Z","updated_at":"2026-10-14T16:56:21.285299Z","company_name":"Zoox","company_slug":"zoox","company_logo_url":"https://www.google.com/s2/favicons?domain=zoox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e8dcc33f-fb7f-4adc-ba9d-72d06ffc5f5f"},{"id":"648b541e-303a-4885-9cd1-6ee5559a1bb9","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Staff Software Engineer, Secure Execution","slug":"staff-software-engineer-secure-execution-2393d7d4","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nAs a Staff Software Engineer on the Security Engineering team, you will join as a founding team member and build the security foundations that let Harvey put increasingly capable AI agents to work. You'll lead development of a platform for agents to perform security work, while shaping how agents are sandboxed across our product and internal platforms.\n\n\n\nOne of the central challenges is enabling models with advanced cybersecurity capabilities to discover and validate vulnerabilities automatically, including through authorized penetration testing, while protecting sensitive data and systems. You'll build the execution environments, orchestration, and controls that make this possible, and help enable the internal use of open-source models with appropriate protections around inference, tool use, and data access.\n\n\n\nYou'll work closely with the teams building Harvey's product sandbox and internal AI agent platform, shaping architecture and contributing production code to strengthen execution boundaries. Your impact will come from shipping systems, establishing a shared technical direction, and making security capabilities reusable across teams. You'll own work from design through rollout and operation, balancing containment with the reliability, performance, and flexibility that useful agents need.\n\n\n\n\nWHAT YOU'LL DO\n\n - Set the technical direction for secure agent execution, working across Security, Infrastructure, and Product Engineering to turn emerging capabilities and risks into a concrete roadmap.\n\n - Design, build, and operate a platform for security agents to discover and validate vulnerabilities within authorized targets and execution boundaries, producing reproducible evidence that engineers can act on.\n\n - Build reusable controls for agent tool use, code execution, network and filesystem access, resource consumption, and workload lifecycle. Integrate with identity and secrets platforms to limit access to the data and credentials each task needs.\n\n - Partner with the owners of Harvey's product sandbox and internal agent platform to make architectural decisions, implement protections, and integrate shared security capabilities into their execution environments.\n\n - Enable internal use of open-source models alongside infrastructure teams, building protections around model serving, agent execution, and sensitive-data handling.\n\n - Develop adversarial tests, evaluations, and telemetry that verify containment and expose failures. Build mechanisms to scope, observe, interrupt, and safely terminate agent activity.\n\n - Lead delivery and adoption across teams, mentor engineers, and remain hands-on through implementation and production operation, making clear tradeoffs across security, reliability, latency, and cost.\n\n\n\n\nWHAT YOU HAVE\n\n - 10+ years developing and running production software, including technical leadership on initiatives spanning multiple engineering teams.\n\n - Strong software engineering skills in languages such as Go, Rust, Python, or C++, with practical experience in Linux systems, networking, and containerized infrastructure.\n\n - Deep expertise in one or more areas of execution security, such as sandboxing, operating-system isolation, container or virtual-machine security, or platforms that execute untrusted code. You can translate threats into enforceable boundaries and test how those boundaries fail.\n\n - Experience building shared platforms, services, or libraries and helping other teams adopt them through clear interfaces, documentation, and safe migrations.\n\n - Experience with cloud infrastructure and distributed systems, including observability, failure handling, capacity management, and dependable production operation.\n\n - Fluency with AI-assisted engineering and","salary_min":231000,"salary_max":346400,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["security","cloud","distributed-systems","agents","mlops"],"apply_url":"https://jobs.ashbyhq.com/harvey/60d3a393-50bc-4f1c-88b7-df6d2507ceda/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T19:51:00.151Z","expires_at":"2026-11-13T16:55:18.334422Z","created_at":"2026-10-14T15:19:08.92027Z","updated_at":"2026-10-14T16:55:18.33739Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/648b541e-303a-4885-9cd1-6ee5559a1bb9"},{"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":"4939555c-c4ef-4776-b87e-a2fdd8a1f17c","company_id":"099a8aa7-a1bc-4a90-aa8d-f59f677b925a","title":"Senior AI Software Engineer, Perception","slug":"senior-ai-software-engineer-perception-36ebc12d","description":"Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential.\n  \n Agility Robotics builds Digit, the world's first commercially deployed humanoid robot — already at work in real warehouses and manufacturing facilities today. Digit has proven itself in structured work cells, and now we're pushing beyond them: into open floors, shared aisles, and dynamic spaces where people, forklifts, and other robots are all moving at once. That shift raises the bar for perception dramatically — Digit needs to reliably understand and react to environments that are no longer predictable or controlled. \n That means perception isn't just about capability — it's about trust. Every person Digit works near is counting on our perception systems to see them, understand them, and never get it wrong, whether Digit is in a fixed work cell or navigating a busy, ever-changing facility. We're looking for a Senior AI Software Engineer, Perception to own the design and implementation of the algorithms that let Digit perceive its surroundings and stay safe as we take on these more dynamic, less constrained environments. \n What You'll Do \n \n Design, develop, and deploy perception algorithms (e.g., human detection algorithm) that enable safety and efficiency, defining how Digit perceives and reasons about humans and hazards as it expands from structured work cells into open, dynamic environments \n Drive adoption of state-of-the-art perception models by evaluating emerging research and technology and deciding what's worth building into future robot architectures \n Promote best practices in model design, implementation and testing to deliver high-quality, scalable software \n Optimize deep neural networks and associated data pipelines to run efficiently on embedded systems (e.g., model structure optimization, CUDA inference etc.) \n Set standards for verification and validation of ML-based perception models, especially for rare and safety-critical scenarios \n Collaborate across navigation, controls, and hardware teams to ensure the perception model serves the whole robot \n \n What You Bring \n \n 5+ years of experience in machine learning/perception for robotics \n Master's or Ph.D. in Artificial Intelligence, Robotics, Computer Science, or a related discipline, with a strong foundation in machine learning, robotics, and intelligent systems \n Demonstrated experience building ML/perception systems for safety-critical or safety-rated contexts — human detection, automotive, robotics, or a comparable regulated domain \n Deep expertise in convolutional neural networks, multi-object tracking, human detection, data association, supervised learning, and pose estimation, with strong intuition for where these approaches break down in unpredictable, real-world conditions \n Experience designing and optimizing algorithms for efficient execution across CPU and GPU architectures \n Experience with MLOps, including data annotation services, data storage, model evaluation tools, and model deployment \n Publications in the field (CVPR, ICCV, RSS, ICRA preferred) \n \n Nice to Haves \n \n Experience designing and implementing perception systems for humanoid or legged robots specifically \n Experience building systems based on multiple sensor modalities including camera, lidar, infrared camera.  \n Knowledge in traditional geometric computer vision and sensor hardware etc. \n \n  \n \n  \n This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices. \n The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future. \n Anticipated Salary Range\n $197,000 — $307,000 USD \n In addition to base pay, our competitive total rewards package consists of the following for full-time employees: \n \n 401(k) Plan:   Includes a 6% company match.\n Equity:   Company stock options.\n Insurance Coverage:   100% company-paid medical, dental, vision, and short/long-term disability insurance for employees.\n Benefit Start Date:   Eligible for benefits on your first day of employment.\n Well-Being Support:   Employee Assistance Program (EAP).\n Time Off: \n \n Exempt Employees:   Flexible, unlimited PTO and 12 company holidays, including a winter shutdown.\n Non-Exempt Employees:   10 vacation days, paid sick leave, and 12 company holidays, including a winter shutdown, annually.\n \n On-Site Perks:   Catered lunches four times a week and a variety of healthy snacks and refreshments at our Salem and Pittsburgh locations.\n Parental Leave:   ","salary_min":197000,"salary_max":307000,"location":"Pittsburgh, PA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","computer-vision","gpu","data-pipeline","robotics","mlops"],"apply_url":"https://www.agilityrobotics.com/about/job-post?gh_jid=6210999004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T16:15:10Z","expires_at":"2026-11-13T16:59:26.118513Z","created_at":"2026-10-14T15:22:58.177037Z","updated_at":"2026-10-14T16:59:26.121355Z","company_name":"Agility Robotics","company_slug":"agility-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=agilityrobotics.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4939555c-c4ef-4776-b87e-a2fdd8a1f17c"}],"page":1,"per_page":20,"total":499,"total_is_exact":true,"total_pages":25}