{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","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","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"acccc186-1fa0-414d-bb62-82bf62875fe7","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Senior Machine Learning Engineering Manager","slug":"senior-machine-learning-engineering-manager-74e24ae5","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 Why Safety AI Systems? \n As Senior Engineering Manager for Safety AI Systems at Roblox, you'll lead technical efforts and manage a team of experienced engineers to develop innovative AI solutions for multimodal content safety. You’ll oversee machine learning systems, constructing multimodal model architectures, improving data quality, training pipelines, and model performance to address challenges like real-time multi-verse content understanding and advanced moderation with large vision language models, spanning avatars, images, videos, audios, text, code / data models, and their composites. \n In close collaboration with product, policy, and Trust \u0026 Safety teams, you'll design large-scale systems to detect and mitigate abusive behavior before it harms the community. You'll own critical services at massive scale, balancing user freedom with platform civility to protect and empower our users. Your leadership will help ensure Roblox remains a safe, inclusive space for self-expression and shared experiences.\n You Will \n \n Own the vision, technical direction, and execution of machine learning solutions for the Multimodal Safety AI system, ensuring these systems effectively detect and prevent harmful content at scale.\n Lead and grow a high-performing team of ML engineers, fostering a culture of innovation, technical excellence, accountability, and inclusivity, while mentoring and developing talent.\n Break down ambitious long-term goals into an actionable, iterative roadmap – delivering continuous improvements in stages and driving tangible value at each step.\n Architect and guide the development of large-scale machine learning models with innovative architectures, ensuring they achieve high quality and are production-ready.\n Drive alignment on complex technical decisions across multiple teams (within and beyond the Safety org), demonstrating empathy and building consensus among diverse stakeholders.\n Collaborate cross-functionally with Product, Data Science, Policy, Design, and Operations partners to define and prioritize the machine learning roadmap for multimodal safety initiatives, ensuring alignment with broader Safety and Roblox objectives.\n Stay ahead of emerging trends in AI/ML and content moderation techniques, continuously innovating our safety approaches to anticipate new challenges. \n \n You Have \n \n 5+ years of experience building large-scale machine learning systems in production environments.\n Proven track record of designing, developing, and launching ML models from scratch into production.\n 2+ years of hands-on experience with vision language models or other foundation model technologies.\n Expertise in solving complex ML modeling, data, and infrastructure challenges – with a focus on maintaining high quality and velocity at scale.\n Ability to thrive in ambiguity: you excel at bringing clarity and direction to undefined or open-ended problem spaces.\n Demonstrated success collaborating across functions (e.g. Product, Design, Data, Research), working together to drive meaningful business and user impact.\n Strong product sense: able to establish clear success metrics and craft strategic roadmaps to achieve those goals.\n High emotional intelligence: adept at resolving conflicts, mentoring engineers, and nurturing the growth of your team members.\n Experience with modern microservice architectures and distributed systems programming paradigms (e.g. cloud services, scalable data pipelines).\n Hands-on to dive into code/architecture and guide technical discussions when needed, in addition to high-level planning. \n \n You Are \n \n Creative and strategic problem-solver: able to distill complex issues into simple, innovative solutions that drive impact.\n An owner: you take responsibility for projects and outcomes end-to-end, and instill the same accountability in your team.\n A lifelong learner: constantly seeking new knowledge and techniques to grow your expertise and expand your impact.\n Independent and self-directed: capable of charting a course with minimal guidance, and comfortable making decisions in uncertain situations.\n Highly collaborative: effective at working with cross-functional partners and teams, and skilled a","salary_min":295250,"salary_max":345040,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","generative-ai","microservices","distributed-systems","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/8047877?gh_jid=8047877","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T02:50:02Z","expires_at":"2026-08-15T14:18:31.254864Z","created_at":"2026-07-16T14:18:31.377819Z","updated_at":"2026-07-16T14:18:31.377819Z","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/acccc186-1fa0-414d-bb62-82bf62875fe7"},{"id":"a9a323aa-3837-447e-9113-5ce18e0a75e1","company_id":"92df3417-f362-4f1a-9406-e34d8013b283","title":"Senior Machine Learning Engineer, Applied AI Quality","slug":"senior-machine-learning-engineer-applied-ai-quality-2d344917","description":"Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block.\n The Role \n At Block, we believe product quality is foundational to great user experiences, and AI is transforming how we measure, understand, and improve that quality at scale. Our team builds the intelligence layer that evaluates system behavior across millions of real-world interactions, helping ensure our products are reliable, safe, and continuously improving.\n We’re looking for a Senior Machine Learning Engineer to lead the technical direction of next-generation quality systems powered by LLMs and AI agents. You’ll drive the architecture and strategy behind systems that evaluate product behavior, surface emerging issues, generate actionable insights, and enable teams across Block to make higher-confidence product decisions.\n In this role, you’ll operate across ambiguous, high-impact problem spaces and shape how quality is measured and operationalized across the organization. You’ll work across engineering, product, platform, and leadership teams to define long-term technical direction, establish scalable evaluation frameworks, and build systems that become foundational infrastructure for AI-driven product quality.\n You Will \n \n Lead the technical strategy and architecture for AI-driven quality and evaluation systems used across products and teams.\n Drive the development of scalable systems that use LLMs, agents, and behavioral signals to evaluate quality, detect regressions, and generate product insights.\n Define long-term approaches for evaluation, measurement, and quality intelligence across complex product surfaces.\n Translate ambiguous organizational needs into clear technical direction, roadmap priorities, and platform capabilities.\n Influence engineering standards and best practices for building reliable, measurable, and trustworthy AI systems.\n Lead complex cross-functional initiatives spanning product, infrastructure, data, and applied AI teams.\n Mentor and level up engineers across the organization through technical leadership, design reviews, and systems thinking.\n Identify leverage opportunities where AI systems can fundamentally improve how teams understand, debug, and improve product behavior.\n \n You Have \n \n 5+ years of experience in software engineering, machine learning engineering or applied AI.\n Deep experience designing and shipping large-scale AI/ML systems in production environments.\n Strong expertise with LLMs, agents, evaluation systems, retrieval architectures, and modern AI infrastructure.\n Proven ability to lead ambiguous, high-impact technical initiatives from concept through adoption across multiple teams.\n Strong systems thinking and architectural judgment, with the ability to balance experimentation, scalability, and operational rigor.\n Experience defining technical strategy and influencing roadmaps beyond your immediate team.\n Excellent communication and cross-functional leadership skills, with the ability to align engineering, product, and organizational priorities.\n A track record of creating leverage through platforms, frameworks, and systems that enable other engineers and teams to move faster and make better decisions.\n \n We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances. We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we’re doing to build a workplace that is fair and square? Check out our I+D page .\n While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.\n  \n Block takes a market-based approach to p","salary_min":194500,"salary_max":291700,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","agents","llm","machine-learning"],"apply_url":"http://block.xyz/careers/jobs/5243440008?gh_jid=5243440008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T17:13:58Z","expires_at":"2026-08-15T14:10:10.409887Z","created_at":"2026-07-16T14:10:10.562945Z","updated_at":"2026-07-16T14:10:10.562945Z","company_name":"Block","company_slug":"block","company_logo_url":"https://www.google.com/s2/favicons?domain=block.xyz\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a9a323aa-3837-447e-9113-5ce18e0a75e1"},{"id":"8e714354-f0cc-4558-b706-5f155771b9bb","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Technical Lead Manager, Machine Learning Runtime \u0026 Serving","slug":"technical-lead-manager-machine-learning-runtime-serving-3bc85bbd","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 Waymo is seeking a senior Technical Lead Manager (TLM) Machine Learning Engineer to guide the technical vision of our core ML infrastructure. In this role, you will actively grow and manage a high-performing team of 6 engineers to deliver Waymo’s next-generation ML ecosystem. This critical work encompasses both the in-vehicle inference engine and the cloud-based serving infrastructure for our foundational models. You will architect scalable, high-performance ML runtime systems that operate across two extreme domains: the highly constrained edge compute environment of autonomous vehicles and our large-scale, offboard data centers.\n You will: \n \n Guide the technical vision of our core ML infrastructure while actively growing and managing a high-performing team of 6 engineers to deliver Waymo’s next-generation ML ecosystem, encompassing both the in-vehicle inference engine and the cloud-based serving infrastructure for our foundational models.\n Architect scalable, high-performance ML runtime systems that operate flawlessly across two extreme domains: the highly constrained edge compute environment of autonomous vehicles and our large-scale, offboard data centers.\n Navigate complex engineering trade-offs, driving feature development that seamlessly balances the strict, real-time latency and memory limits of onboard execution with the high-throughput, highly concurrent demands of fleet-scale cloud serving.\n Spearhead the strategic transition of core ML workloads to a JAX-native runtime architecture, which includes actively extending and modifying underlying ML compilers and runtimes (e.g., OpenXLA/PjRT, TensorRT).\n Partner across organizational boundaries with world-class ML researchers in Perception and Planning to deeply analyze system-level workloads and unlock massive performance gains through hardware-aware compute optimizations.\n Drive systemic performance excellence by designing advanced profiling and benchmarking infrastructure to identify, triage, and eliminate bottlenecks across the entire end-to-end ML software stack.\n \n You have: \n \n B.S. or M.S. in CS, EE, Deep Learning or a related field.\n People management experience, with a proven track record of recruiting, mentoring, and guiding high-performing teams of senior engineers.\n 8+ years of professional software engineering experience architecting, building, and scaling complex ML systems and infrastructure.\n Strong production programming expertise.\n Proven track record of optimizing ML software to maximize the performance of hardware accelerators (e.g., GPUs, TPUs, or custom silicon).\n Hands-on experience developing distributed backend systems that are low-latency, highly concurrent, and fault-tolerant at scale.\n \n We prefer:  \n \n PhD in CS, EE, Deep Learning or a related field.\n Deep expertise in modifying and extending ML software stacks, including compilers, runtimes, or inference engines (e.g., OpenXLA/PjRT, TensorRT, ONNX Runtime, TVM).\n Strong background in building and scaling LLM serving systems, leveraging advanced distributed inference and performance optimization techniques.\n Deep expertise in edge computing and automotive ML deployment, navigating strict power, thermal, and real-time latency constraints to optimize and deploy mission-critical models on resource-constrained embedded hardware.\n \n \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","llm","autonomous-vehicles","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8062303","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T01:09:13Z","expires_at":"2026-08-15T14:05:15.597814Z","created_at":"2026-07-15T14:06:33.079101Z","updated_at":"2026-07-16T14:05:15.721462Z","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/8e714354-f0cc-4558-b706-5f155771b9bb"},{"id":"66be6f1d-738c-4b9b-b07d-4cae69e7b29d","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Machine Learning Engineer, Agent Oversight","slug":"senior-machine-learning-engineer-agent-oversight-774633fc","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Machine Learning Engineer on Agent Oversight, you will drive the end-to-end lifecycle that ensures our production agents perform reliably and improve over time. This includes building observability tools, designing robust evaluation frameworks, and developing improvement loops. Whether scaling infrastructure or researching new improvement methods, you will navigate the entire ML loop while maintaining rigorous technical standards.\n You will:\n \n Build or contribute to observability into agent behavior in production — the signals and instrumentation needed to actually see what an agent is doing, not just whether it succeeded or failed\n Design evaluation methodologies and metrics for agentic applications, and work with the platform to make them run automatically, at scale, across different customer use cases, not just as one-off analyses\n Build, ship, and own ML systems that detect drift, anomalies, or misalignment in production agent behavior — from first prototype through running reliably at scale\n Design and run rigorous experiments to validate model and agent performance improvements before they ship\n Work alongside software engineers on the platform where your work intersects with broader infrastructure — but you’re expected to take your own work from idea to production, not hand it off\n Collaborate closely with product managers, customers, data annotators, Forward Deployed Engineers, and other engineering teams to translate enterprise and government requirements into robust platform capabilities\n Depending on focus, contribute to novel methods and approaches that push the state of the art for agent evaluation and improvement, or focus on building ML systems that hold up reliably at scale in production\n \n Requirements:\n \n 5+ years of experience as an ML engineer or applied scientist, ideally on a production ML or LLM-powered system — not just consuming a third-party ML API within a feature\n Strong grounding in  at least two  of the following:\n \n Building or scaling evaluation, monitoring, or continuous-learning infrastructure for ML/agentic systems\n Design experience for agent systems (architecture, orchestration, tool use)\n Developing new methods, reward models, or model training/fine-tuning approaches\n \n Hands-on experience with LLMs and agent architectures — tool use, planning, multi-agent orchestration\n Comfortable partnering with software engineers to productionize research and experimental work, not just deliver a one-off analysis\n Rigorous approach to experimentation: clear hypotheses, real statistical grounding, and results that hold up under scrutiny\n Track record of collaborating across functions (Product, Forward Deployed Engineering, etc.) to navigate ambiguous requirements and bring them to production\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Experience building or contributing to RLHF, SFT, or other fine-tuning/RL workflows, reward modeling, or verifiable-reward systems\n Experience with model or systems optimization (e.g., latency, cost, or inference efficiency)\n Published research, open-source contributions, or patents in agentic systems, LLMs, or applied ML\n Experience working in regulated or enterprise contexts\n Track record of taking a novel method from prototype to something running reliably in production, navigating ambiguity along the way\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career level","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","reinforcement-learning","agents","generative-ai","autonomous-vehicles","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714527005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:14:32Z","expires_at":"2026-08-15T14:01:43.524986Z","created_at":"2026-07-15T14:01:47.280877Z","updated_at":"2026-07-16T14:01:43.716585Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/66be6f1d-738c-4b9b-b07d-4cae69e7b29d"},{"id":"dbf3ed2d-61a8-46d4-8f99-13cd04d28f0e","company_id":"4c109027-78ec-41cd-b57a-dc58e47d0bd0","title":"Senior Machine Learning Scientist I, Model-Driven Optimization","slug":"senior-machine-learning-scientist-i-model-driven-optimization-eb80e6bd","description":"The Role:  \n Generate:Biomedicines is seeking a creative, rigorous, and execution-oriented machine learning scientist to join our Model-Driven Design team. This role will focus on building the ML methods, data strategies, and closed-loop systems that determine what we design, build, test, and learn from next.\n The Model-Driven Design team works at the interface of machine learning, protein design, engineering, and experimental science. We develop and apply models and quantitative frameworks that help Generate discover and optimize therapeutic proteins. In this role, you will help advance the technical foundation of our lab-in-the-loop protein optimization platform, with a focus on sequential decision-making, experimental design, property modeling, and scalable design systems.\n We are looking for someone who can serve as a technical leader and hands-on individual contributor, driving complex, high-impact work from problem framing through implementation, deployment, and experimental impact. The ideal candidate combines depth in probabilistic machine learning, Bayesian optimization, active learning, or related approaches with the practical judgment and engineering discipline to turn technical ideas into reliable systems that drive impact. You will partner closely with protein designers, wet-lab scientists, ML scientists, and engineers to build durable capabilities that accelerate therapeutic discovery.\n This role is part of a highly collaborative team environment that balances in-person collaboration with hybrid flexibility based out of our Somerville, MA office. \n Here's how you will contribute: \n \n Develop new machine learning methods and systems for lab-in-the-loop protein optimization, including property models and multi-objective optimization strategies for therapeutic protein design.\n Shape data-generation and data-use strategies that make experimental campaigns maximally informative for model improvement, therapeutic optimization, and future design cycles.\n Build and apply LLM-enabled and agentic workflows that help scientists explore design hypotheses, connect models to data and experiments, and accelerate iterative learning.\n Design, implement, test, and maintain production-quality ML models, software components, and data workflows, with attention to reliability, reproducibility, observability, and computational efficiency.\n Partner with ML engineering and software teams to integrate these components into robust, scalable platform capabilities, with clear ownership across team boundaries.\n Collaborate closely with protein designers and wet-lab scientists to ensure models and optimization systems are grounded in experimental reality and deliver measurable impact.\n Identify important technical gaps, develop proposals, define milestones, align stakeholders, and help set technical direction across cross-functional programs.\n Communicate clearly across disciplines and help raise technical standards across ML, engineering, protein design, and experimental teams.\n \n The Ideal Candidate will have: \n \n PhD in machine learning, computational biology, computer science, applied mathematics, engineering, or a related quantitative field.\n Strong practical experience with probabilistic machine learning, Bayesian optimization, active learning, experimental design, or related approaches for sequential decision-making under uncertainty.\n Experience developing machine learning methods or systems for biological, biomedical, or experimental scientific data, with an ability to reason about noisy assays, sparse labels, experimental bias, and data-generation strategy.\n Demonstrated ability to translate ML ideas into systems, tools, or workflows that affect real scientific, experimental, or product decisions.\n Strong Python skills and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.\n Strong systems thinking and ability to design technical interfaces, reason about system tradeoffs, and partner with engineering teams to build scalable, maintainable ML infrastructure.\n Excellent communication skills and ability to bridge ML, engineering, protein design, and experimental stakeholders.\n Pragmatic, collaborative working style, with the ability to bring structure to open-ended problems and balance scientific rigor with execution in fast-moving, cross-functional environments.\n \n Nice to have \n \n Experience in protein design, protein engineering, antibody engineering, biologics discovery, or drug development.\n Experience partnering with experimental teams on design-build-test-learn cycles, high-throughput screening, directed evolution, pooled libraries, or model-guided experimental campaigns.\n Experience with multi-objective optimization, uncertainty calibration, model-guided library design, or experimental campaign planning.\n Experience developing and applying deep learning models, including transformer-based architectures\n Experience building or applying LLM agents, scientific copilots, or agentic syste","salary_min":192000,"salary_max":265000,"location":"Somerville, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","pytorch","healthcare","deep-learning","code-generation","machine-learning"],"apply_url":"https://generatebiomedicines.com/open-positions?gh_jid=4696856006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T19:16:51Z","expires_at":"2026-08-15T14:14:34.598877Z","created_at":"2026-07-15T14:15:56.439634Z","updated_at":"2026-07-16T14:14:34.720622Z","company_name":"Generate Biomedicines","company_slug":"generate-biomedicines","company_logo_url":"https://www.google.com/s2/favicons?domain=generatebiomedicines.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dbf3ed2d-61a8-46d4-8f99-13cd04d28f0e"},{"id":"02fdc710-8e20-40fd-aedd-05f740fa50ac","company_id":"377b9ca2-ac79-48a5-8657-da630f9e447d","title":"Senior Staff / Principal Machine Learning Scientist, AI Inference \u0026 Optimization","slug":"senior-staff-principal-machine-learning-scientist-ai-inference-optimization-8c8ecaa7","description":"About Netskope \n Today, there's more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security. \n \n Since 2012, we have built the market-leading cloud security company and an award-winning culture powered by hundreds of employees spread across offices in Santa Clara, St. Louis, Bangalore, London, Paris, Melbourne, Taipei, and Tokyo. Our core values are openness, honesty, and transparency, and we purposely developed our open desk layouts and large meeting spaces to support and promote partnerships, collaboration, and teamwork. From catered lunches and office celebrations to employee recognition events and social professional groups such as the Awesome Women of Netskope (AWON), we strive to keep work fun, supportive and interactive.     Visit us at  Netskope Careers. Please follow us on LinkedIn and Twitter @Netskope . \n Positions are available at Senior Staff and above. Candidates are assessed individually and leveled according to their specific skills and background. \n About the role\n As a Senior Staff Machine Learning Scientist, you own the inference and optimization layer that makes AI in agentic workflows fast, efficient, and production-grade. You fine-tune and evaluate models, push latency and throughput on real hardware, and build the runtime that executes bounded AI tasks, validated against usage from Netskope’s large customer base so you optimize where the data points, not where you guess.\n What’s in it for you\n \n High-impact ownership. You own the model layer of a net-new product that changes the performance and economics of agentic AI.\n Cutting-edge, unusual stack. The hard, interesting inference problems live here: quantization, KV-cache and memory management, sparsity, fine-tuning, and hardware acceleration under real-world resource constraints.\n Real scale to build against. Netskope’s customer footprint gives you production signals most teams never see, so you deploy, validate, and iterate fast.\n \n What you will be doing\n \n Build and optimize the model inference path : quantization, KV-cache optimization, batching, and latency/memory/throughput tuning on constrained, commodity hardware.\n Fine-tune and evaluate models for bounded tasks; build eval harnesses that gate a capability to release on real accuracy, latency, and security relevance.\n Design and grow the task execution runtime (bounded sub-agents), pushing toward dynamic task generation and context compaction.\n Drive hardware acceleration / sparsity and support for larger models as the platform matures.\n Partner with the systems and backend engineers to ship capabilities end-to-end and iterate on real production signals.\n \n Required skills and experience\n \n 10+ years of overall industry experience , with 4+ years hands-on in ML/AI (model development, fine-tuning, and inference optimization).\n Hands-on with fine-tuning (e.g. LoRA/QLoRA), quantization (GGUF/AWQ/GPTQ), and inference runtimes (vLLM/SGLang, TensorRT-LLM, ONNX Runtime, llama.cpp, or MLX/CoreML). On-device or edge inference experience is a strong plus.\n Strong Python; comfort reaching into C++ for low-level interop is a plus.\n Solid grasp of transformer internals and the levers that move real inference performance and cost: KV cache, attention, batching, memory footprint.\n Fluency with agentic coding systems and genuine curiosity about agent harnesses like Claude Code, Pi, and Codex , so you should already be building with them, or itching to.\n Clear communication: able to distill a model or infra bottleneck into an actionable concept for cross-functional teammates.\n \n Education\n \n MS in Computer Science, Machine Learning, Electrical Engineering, or equivalent technical degree required, with a focus in AI/ML research; PhD in a related field strongly preferred.\n Compensation:  \n At Netskope, salary is one component of our competitive total rewards package. The salary range for this position is as listed below. This is a national range. For purposes of complying with applicable laws, the range applies to candidates in California, Colorado, Illinois, Maryland, New York, Washington, and other states. \n The successful candidate’s starting pay will also be determined based on job-related skills, experience, qualifications, location, and market conditions.  \n For all sales roles, the posted salary range is the On Target Earnings (OTE) range for the role, which is the sum of base salary and target commission amount at 100% goal achievement. \n In addition to salary, candidates may be eligible for other forms of compensation such as participation in a bonus plan (for non-sales roles) and a stock award program. Candidates may also be eligible for a comprehensive health plan and other benefits that can be reviewed at  Netskope Benefits site .","salary_min":182500,"salary_max":260500,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["llm","cloud","agents","fine-tuning","machine-learning","inference"],"apply_url":"https://www.netskope.com/company/careers/open-positions/?gh_jid=8063869","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T04:20:32Z","expires_at":"2026-08-15T14:10:20.211022Z","created_at":"2026-07-15T14:11:39.076302Z","updated_at":"2026-07-16T14:10:20.351571Z","company_name":"Netskope","company_slug":"netskope","company_logo_url":"https://www.google.com/s2/favicons?domain=netskope.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/02fdc710-8e20-40fd-aedd-05f740fa50ac"},{"id":"e6cf414e-5202-4a66-9735-43bcbeb83352","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Senior Machine Learning Engineer, Ads Content Understanding","slug":"senior-machine-learning-engineer-ads-content-understanding-06e45727","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 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Reddit has a flexible workforce!  If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence.\n Ads Content Understanding (ACU) owns and produces signals that describe what Reddit content is about, how brand safe and suitable it is, and what users are trying to accomplish in commercial conversations. ACU is responsible for:\n \n The Knowledge Graph (entities, brands, products, and relationships across Reddit and external sources).\n Content taxonomies such as IAB, Shopify Standard Product Taxonomy, IAS, and other commercial taxonomies used for targeting, safety, and marketplace dynamics.\n Opinion mining for ads use cases: sentiment, stance, commercial intent, and other qualitative attributes of conversations.\n Shopping / product understanding: detecting product entities, product categories, and product attributes in organic conversations and aligning them with shopping catalogs.\n Signals and tags registry: a unified, governed catalog of ACU signals that powers retrieval, ranking, safety, and insights across Ads Foundations and partner teams. \n \n We are looking for a Senior Machine Learning Engineer (IC4) who will act as a key contributor to the Content Understanding roadmap for the Monetization org.\n This is not a research scientist or pure DS role; success is defined by robust, shipped systems and monetization impact.  The ideal candidate is a pragmatic engineer with strong software engineering fundamentals and solid ML intuition—not a pure research scientist. This is an Applied MLE role, requiring someone who can evaluate when to leverage hosted LLMs versus custom models, help scale content understanding to new modalities (e.g., video), and drive practical ML solutions that deliver business impact. \n Responsibilities: \n \n Operate across the full ML lifecycle (problem framing, data, modeling, evaluation, deployment, monitoring, and oncall), designing scalable ML pipelines and championing responsible AI (bias, safety, explainability) for ACU’s models and signals in production.\n Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU, design reviews, setting technical standards, and uplifting the team’s modeling and systems craft.\n Develop evaluation systems and quality monitoring systems for content understanding signals, using SOTA LM-judge practices. \n Drive operational excellence for ACU’s ML systems by defining SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost)\n Build and evolve content understanding capabilities for commercial conversations (e.g., reviews vs. recommendations vs. comparisons vs. Q\u0026A; sentiment and stance; product entities and categories) and operationalize them as robust signals that power contextual and shopping ads, auto-targeting, new formats, and insights products.\n Drive LLM and modern ML best practices within ACU: define when to prompt, finetune, or distill; design evaluation and safety harnesses; and lead at least one major distillation effort to replace external APIs with in-house models.\n \n Required Qualifications: \n \n 5+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains, or Ads. \n Demonstrated Senior-level technical leadership: has contributed to architecture decisions, standards, and design reviews in their immediate team\n Strong communication skills, with the ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams, especially in ambiguous, cross-team problem spaces like Seekers/Searchers monetization. \n Some experience building and shipping NLP / Language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models), with clear business outcomes (e.g., CTR/ROAS uplift, safety improvements). Experience with commercial or intent modeling is a strong plus.\n \n Preferred Qualifications: \n \n Practical experience using LLMs in production for labeling, evaluation, or distillation (e.g., LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs.\n Significant experience with PyTorch, TensorFlow, or similar, and production-quality code in Python (and ideally one staticall","salary_min":216700,"salary_max":303400,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["healthcare","pytorch","llm","tensorflow","nlp","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8008648","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:21:59Z","expires_at":"2026-08-15T14:09:28.637905Z","created_at":"2026-07-15T14:10:37.261524Z","updated_at":"2026-07-16T14:09:28.758067Z","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/e6cf414e-5202-4a66-9735-43bcbeb83352"},{"id":"71ee758f-16cd-4c69-8fc0-3f12619c37ad","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff Machine Learning Engineer, Simulation ","slug":"staff-machine-learning-engineer-simulation-353a3819","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Driver Understanding and Evaluation team at Waymo develops a rich understanding of Waymo Driver's behavior. With over 1 million driverless miles per week, it is critical that Waymo can understand and assess the behavior of all its vehicles - both in the field and in simulation - with automated algorithms. The learned metrics team is a strategic bet to use machine learning to ensure we can scale to meet Waymo's goals. We collaborate across teams to bring ML to production systems and build what is Waymo's reward function. We build and operate large-scale machine learning and data systems, simulation workflows, and insight tools. We combine expert human judgements and advanced machine learning models to deliver training and evaluation data for the Waymo driver. We are looking for researchers and software engineers who are passionate about developing production grade machine learning systems for our autonomous vehicles and have an incessant drive to improve the performance of our technology stack.\n In this hybrid role, you will report to an Engineering Manager  \n You will: \n \n Report into the TLM for the Learned Metrics Team\n Develop ML models that assess our autonomous vehicle's behavior.\n Develop ML infrastructure to support performant models.\n Collaborate across teams to bring state-of-the-art to production.\n \n You have: \n \n BS in Computer Science, Robotics, Statistics, Physics, Math or another quantitative area, or equivalent work experience\n 5+ years of experience building productionized ML models\n Code and design skills: comfort building production systems (Python / C++)\n Background in applied Deep Learning\n A track record in improving model quality\n \n We prefer: \n \n 8+ years of experience building productionized ML models\n Experience in reinforcement learning, transfer learning, or learning.\n Experience with large scale data and models\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 $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","deep-learning","autonomous-vehicles","robotics","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8056720","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:11:43Z","expires_at":"2026-08-15T14:05:13.85177Z","created_at":"2026-07-15T14:06:31.242063Z","updated_at":"2026-07-16T14:05:13.968294Z","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/71ee758f-16cd-4c69-8fc0-3f12619c37ad"},{"id":"e57cf48d-3756-4016-8e50-400a76bbaa5d","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Computer Vision","slug":"staff-machine-learning-engineer-computer-vision-147d8a7f","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development. Labs works across a broad variety of AI/ML initiatives—including core computer vision, multimodal representation learning, heterogeneous graph neural networks, generative modeling, and recommender systems. This is the group that develops the foundation ML models that fully leverage the tens of billions of Pins and the associated knowledge graph to improve the core product.\n We are currently hiring for the Visual Modeling team in Labs, which develops Pinterest's in-house visual encoder. In this role, you'll work with Pinterest's rich visual-text dataset to train large-scale models from scratch that are continuously shipped to production to power visualization features. You'll build multimodal representations that power applications such as recommender systems, Semantic IDs, and a range of downstream ML models. The visual encoder also produces visual tokens that power our in-house VLM and composed image retrieval models. The core visual pod is a small group (~10 engineers) inside Labs, which allows for deep collaboration. For example, engineers working on multimodal representation also contribute to our internal text-to-image generation Canvas project—collaborating on autoencoder design or on reward function development for RL training.\n  \n What you’ll do: \n \n Prototype state-of-the-art visual encoders that power Pinterest's recommender systems and internal visual language models.\n Experiment with billion-scale datasets and gain hands-on experience with large-scale GPU computing.\n Build flexible visual reasoning tools such as composed image retrieval, promptable detection/segmentation, and instruction-tuned embedding and generative models.\n Read research papers, participate in group discussions, and help brainstorm the company's overall visual generative strategy.\n Help collect relevant visual instruction training data that can be shared across multimodal representation, composed image retrieval, text-to-image generation and visual language modeling.\n Publish and share your work through conferences, paper submissions, and blog posts.\n Mentor junior researchers and research interns within the Pinterest Labs organization.\n  \n \n What we’re looking for: \n \n Research engineers and scientists with experience building and training computer vision models.\n Experience with multimodal representations and visual language modeling is strongly preferred.\n A track record of research contributions (e.g., publications, open-source work) and/or shipping ML models to production.\n Hands-on experience with large-scale model training and modern deep learning frameworks (e.g., PyTorch).\n Strong collaboration skills and a demonstrated ability to work effectively in a small, fast-moving team.\n M.S. or PhD in Machine Learning or related academic areas, or equivalent work experience.\n Publications at top ML conferences\n Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring\n \n  \n Relocation Statement: \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.\n This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.\n \n  \n #LI-REMOTE #LI-AK7\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The posit","salary_min":189308,"salary_max":389753,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["search","deep-learning","generative-ai","code-generation","pytorch","computer-vision","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8015537","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T17:51:37Z","expires_at":"2026-08-15T14:09:24.364141Z","created_at":"2026-07-15T14:10:33.975738Z","updated_at":"2026-07-16T14:09:24.488319Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e57cf48d-3756-4016-8e50-400a76bbaa5d"},{"id":"5e3167da-1058-431f-8718-8bb9f1e4656f","company_id":"3d233526-89a8-48ea-b0ed-3304a35b8acf","title":"Software Engineer II, ML Ops","slug":"software-engineer-ii-ml-ops-461a0523","description":"At WHOOP, we're on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.\nWe are looking for a talented and passionate Software Engineer II to join our MLOps team, focusing on the development and optimization of ML cloud infrastructure. In this role, you will play a critical part in supporting our Data Science and AI teams by building robust, scalable systems for the productionalization of machine learning models. Your work will be at the heart of bringing advanced ML/AI solutions into production, ensuring they are reliable, scalable, and ready to drive value across WHOOP.\n","salary_min":125000,"salary_max":175000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["cloud","mlops","machine-learning","research"],"apply_url":"https://jobs.lever.co/whoop/82635467-6cfb-4e8b-967e-73355a0d0b8f/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T16:24:21.247Z","expires_at":"2026-08-15T14:17:41.659491Z","created_at":"2026-07-15T14:19:08.388557Z","updated_at":"2026-07-16T14:17:41.774926Z","company_name":"WHOOP","company_slug":"whoop","company_logo_url":"https://www.google.com/s2/favicons?domain=whoop.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5e3167da-1058-431f-8718-8bb9f1e4656f"},{"id":"cb44c455-97e8-4e00-ab4f-3fab00fa325f","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-586d7131","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 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Job Duties: Design, develop, and train advanced machine learning models, including deep neural networks, transformer-based architectures, and reinforcement learning systems, to power large-scale online advertising ranking and optimization platforms. Lead the development and optimization of complex feature representations, including high-dimensional embeddings, contextual and temporal signals, and cross-session user behavior modeling. Drive end-to-end model lifecycle execution, including system architecture design, large-scale experimentation, model deployment, performance monitoring, and iterative infrastructure improvements in production environments. Collaborate closely with product, data, and infrastructure engineering teams to translate business objectives into scalable, statistically rigorous modeling solutions. Conduct advanced experiment design and causal analysis to evaluate model impact and inform strategic decisions. Provide technical leadership and mentorship to machine learning engineers and contribute to organization-wide modeling standards, best practices, and long-term technical strategy. Shape the long-term modeling vision across multiple advertising domains, including conversion optimization, application advertising, shopping, and brand advertising. Full-time telecommuting is an option. \n Requirements: Master’s degree in Computer Science, Engineering (any field) or related quantitative discipline and (3) three years of experience in the job offered or related occupation. \n Special Skill Requirements: 1) Python, Java, and Scala; 2) C++, Go, or Rust; 3) major machine learning frameworks and libraries; 4) applied statistics, hypothesis testing and experiment design for online machine learning systems; 5) large-scale data processing and analytics frameworks; 6) deployment and operation of production systems in containerized and distributed environments; 7) Designing and training advanced models, including deep neural networks, transformer-based architectures, and reinforcement learning models; 8) marketplace dynamics, such as real-time bidding (RTB) or pacing control systems; 9) developing and optimizing online advertising systems, including ad ranking, targeting, and market place; 10) providing technical leadership, mentorship, or guidance to other machine learning engineers. Any suitable combination of education, training and/or experience is acceptable. Full-time telecommuting is an option. \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n Submit a resume with references using the apply button on this posting or by email at:  applicationsreview@reddit.com at Req.# 1016.83.2.\n  \n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit  https://www.redditinc.com/careers/ .\n To provide greater transparency to candidates, we share base pay 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. \n The base pay range for this position is: $230,000.00 - $322,000.00 USD\n  \n #LI-DNI\n In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.\n During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We ","salary_min":230000,"salary_max":322000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","deep-learning","mlops","reinforcement-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8054426","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T13:51:13Z","expires_at":"2026-08-15T14:09:30.82854Z","created_at":"2026-07-15T14:10:39.537452Z","updated_at":"2026-07-16T14:09:30.952676Z","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/cb44c455-97e8-4e00-ab4f-3fab00fa325f"},{"id":"90b670fb-c16e-4406-9cd7-c2e700e6570a","company_id":"c0136eba-1fff-477a-8968-c5435a645cd3","title":"Senior AI Infrastructure Engineer - Model Training","slug":"senior-ai-infrastructure-engineer-model-training-1ae1d299","description":"Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.\n Kodiak's AI is only as good as the speed at which we can train it. Every improvement to our models – from GigaFusionNet to large-scale world models – depends on infrastructure that turns thousands of hours of multimodal driving data into training throughput. We are looking for engineers who make model training fast: streaming massive camera, LiDAR, and radar datasets without stalling a single GPU, sharding data and models efficiently across nodes, and extracting every FLOP from the latest hardware. If you measure your impact in tokens per second and GPU utilization, this role is for you. In this role, you will: \n \n Design high-throughput data loading and streaming systems for multimodal sensor data (camera, LiDAR, radar), including dataset formats, sharding strategies, and prefetching pipelines that keep GPUs saturated \n Build and optimize distributed training infrastructure across multi-node GPU clusters, applying data, tensor, pipeline, and fully sharded (FSDP/ZeRO) parallelism to models that don't fit on a single device \n Maximize utilization of modern accelerators such as NVIDIA B200s through mixed-precision training (BF16/FP8), fused kernels, memory optimization, and communication/computation overlap \n Profile end-to-end training pipelines to find and eliminate bottlenecks across storage, network, CPU preprocessing, and GPU compute \n Develop scalable dataset construction pipelines that convert petabytes of raw driving logs into training-ready, streamable formats \n Partner with ML teams to scale new architectures from prototype to full-cluster training runs efficiently and reliably \n \n What you’ll bring: \n \n BS, MS, or PhD in Computer Science or a related field, and at least 2-3 years of industry experience in ML systems or infrastructure \n Hands-on experience with distributed training frameworks and techniques (PyTorch DDP/FSDP, DeepSpeed, Megatron, NCCL) and a strong grasp of parallelism trade-offs \n Experience building high-performance data pipelines for large-scale training, including streaming dataset formats (WebDataset, MosaicML Streaming/MDS, or similar), sharding, and storage/network-aware loading \n Deep understanding of GPU performance: mixed precision, memory hierarchy, kernel fusion, profiling tools (Nsight, PyTorch Profiler), and interconnects (NVLink, InfiniBand) \n Strong Python skills and proficiency in PyTorch internals; systems-level experience (C++/CUDA/Triton) a plus \n Passion for building the infrastructure that lets AI for the physical world train faster, scale further, and improve continuously \n \n What we offer: \n \n Competitive compensation package including equity and annual bonuses \n Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits) \n MetLife Legal Services, Identity \u0026 Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, \u0026 Critical Illness Insurance \n Flexible PTO, 10 paid holidays, and generous parental leave policies \n Our office is centrally located in Mountain View, CA \n Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging \n Long Term Disability, Short Term Disability, Life Insurance \n Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation)  \n Fidelity 401(k) \n Commuter, FSA, Dependent Care FSA, HSA \n Various incentive programs (referral bonuses, patent bonuses, etc.) \n The pay range listed below reflects the base salary  in our SF/Silicon Valley location,  across several internal levels. Actual starting pay will be based on job-related factors including: work location, experience, relevant training, education, skill level and performance during interview. Total compensation at Kodiak includes base pay, equity, bonus and a competitive benefits package\n California Pay Range\n $190,000 — $260,000 USD \n  \n At Kodiak, we strive to build a diverse community working towards our common company goals in a safe and collaborative environment where harassment of any kind is strictly prohibited. Kodiak is committed to equal opportunity employment regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or veteran status,","salary_min":190000,"salary_max":260000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","robotics","data-pipeline","gpu","pytorch","distributed-systems","machine-learning","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/kodiak/jobs/4310775009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-09T16:07:51Z","expires_at":"2026-08-15T14:09:12.860026Z","created_at":"2026-07-10T14:08:04.242712Z","updated_at":"2026-07-16T14:09:12.9833Z","company_name":"Kodiak Robotics","company_slug":"kodiak-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=kodiak.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/90b670fb-c16e-4406-9cd7-c2e700e6570a"},{"id":"4d6c08b7-a823-4c23-8acb-143bb6fe1561","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-3e90db0f","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n The Machine Learning Platform team builds and operates the core infrastructure that powers ML across Toast — the feature store, model hosting and serving, the experimentation platform, training pipelines, and the tooling ML engineers and data scientists rely on every day. Our work directly enables the models that drive personalization, forecasting, fraud detection, search, and the growing set of AI-powered experiences shipping to restaurants.\n Toast is seeking a Staff Software Engineer to act as a technical leader on the ML Platform team, shaping the systems that will carry Toast's ML capabilities into the next decade. The role involves driving architectural direction across the platform, delivering foundational infrastructure that other teams build on, and elevating fellow engineers. The ideal candidate is a domain expert who partners with ML engineers, data scientists, product, and infrastructure leadership on high-leverage opportunities.\n This position suits an engineer comfortable writing production code, leading technical design for distributed systems, and influencing organizational decisions about how Toast builds and deploys ML.\n A day in the life (Responsibilities) \n \n Own technical direction of the ML Platform — feature store, model hosting and serving, experimentation, training infrastructure — driving architectural decisions around scalability, reliability, latency, and cost\n Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams\n Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies\n Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers\n Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps\n Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback\n Leverage AI-augmented development tools to increase development velocity and code quality\n \n What you'll need to thrive (Requirements): \n \n 8+ years delivering complex backend or infrastructure systems at scale\n Direct experience building or operating core ML infrastructure — feature stores, model serving, experimentation platforms, training orchestration, or equivalent\n Mastery of a modern backend language such as Python, Java, Kotlin, Go, or Scala\n Deep proficiency with distributed systems concepts: consistency, latency, throughput, fault tolerance, and observability\n Strong understanding of data modeling, query languages, and the online/offline data patterns that underpin ML systems\n Demonstrated technical leadership, with ability to drive cross-team alignment and influence engineering, product, and business stakeholders\n Bachelor's degree in Computer Science or a related field, or equivalent practical experience\n \n Nice to Haves: \n \n Hands-on experience with open-source or commercial ML platform components (e.g. Tecton, MLflow, SageMaker, Databricks)\n Experience building or operating experimentation / A-B testing platforms at scale\n Familiarity with real-time streaming systems (Kafka, Flink, Spark Streaming) and their use in feature computation\n Experience serving LLMs or large deep-learning models in production, including GPU capacity planning and inference optimization\n Comfort with Kubernetes and modern cloud-native infrastructure\n Prior work supporting internal-developer-facing platforms with a product mindset\n \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-REMOTE\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience","salary_min":151000,"salary_max":242000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["mlops","distributed-systems","llm","machine-learning"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8031086","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-09T14:54:23Z","expires_at":"2026-08-15T14:10:30.587715Z","created_at":"2026-07-10T14:09:17.56432Z","updated_at":"2026-07-16T14:10:30.735679Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4d6c08b7-a823-4c23-8acb-143bb6fe1561"},{"id":"cc4472bd-80d6-4ce0-b4a1-abc0923120e0","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Ads Conversion Core Modeling","slug":"staff-machine-learning-engineer-ads-conversion-core-modeling-6958e589","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n We are looking for a Staff Machine Learning Engineer to lead the technical vision for our Ads Conversion Core Modeling team, building the state-of-the-art systems that power our global marketplace.\n  \n What you’ll do:  \n \n Lead the technical direction and development of state-of-the-art applied ML projects for ads conversion.  \n Design and build large-scale DNN models to improve user action prediction with low latency.  \n Mine text, visual, and user signals to better understand intention and infer interests from online activity.  \n Use AI to accelerate analysis and iteration, while applying judgment and verification to ensure correctness and quality.  \n Automate repeatable tasks such as documentation, reporting, and QA checks to speed up the development lifecycle.  \n Coach and mentor engineers while collaborating with product and sales to design new ad products.\n \n  \n What we’re looking for: \n \n Bachelor's degree in Computer Science, Statistics, or a related field.\n 6+ years of industry experience building production ML systems at scale (Search, Recommendations, or Ranking).  \n 2+ years of experience leading technical projects or teams.  \n Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.  \n Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.\n Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.\n High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final deliverables.\n Strong mathematical foundation and experience with statistical methods and A/B testing. \n \n  \n Relocation Statement:  \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.\n This role will need to be in the office for in-person collaboration 1-2 times per month and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, Seattle.\n \n #LI-HYBRID \n #LI-SM4\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.\n Information regarding the culture at Pinterest and benefits available for this position can be found here . \n US based applicants only\n $222,716 — $389,753 USD \n Our Commitment to Inclusion: \n Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religiou","salary_min":222716,"salary_max":389753,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","fine-tuning","code-generation","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8011452","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T21:09:01Z","expires_at":"2026-08-15T14:09:24.2528Z","created_at":"2026-07-09T14:08:40.4464Z","updated_at":"2026-07-16T14:09:24.405031Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cc4472bd-80d6-4ce0-b4a1-abc0923120e0"},{"id":"40013bc5-a45e-4719-a190-172312e50157","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Merchants ","slug":"staff-machine-learning-engineer-merchants-03feb283","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n We’re hiring a Staff Machine Learning Engineer to help drive the future of merchant presence and shopping experiences on Pinterest.\n This role sits on the Merchant team and focuses on building AI/ML systems (including LLMs) that identify, understand, and surface relevant, high-quality merchants across segments—so Pinners can discover new brands with greater confidence and consideration, and merchants can reach new, diverse audiences.\n In this role, you’ll lead LLM-first, evaluation-driven initiatives—near-term focused on agentic workflows, measurement, and operational rigor that strengthen Merchant Integrity and Business Integrity. Longer term, you’ll help advance core relevance capabilities such as merchant/brand affinity modeling and related signals that improve shopping discovery across Pinterest. You’ll partner closely with Product Managers, Engineering Managers, Data Science, Design, and platform teams to take systems from early prototypes to reliable, scaled production.\n You will also serve as the technical lead for ML in this space—reporting to a Director and acting as the first ML Engineering hire in this org—helping define the technical roadmap, establish engineering standards, and lay the foundation for scaling the domain and team over time. This is a high-agency, high-impact role with direct levers on user trust, relevance, and shopping outcomes across high-traffic Pinterest surfaces (organic and paid).\n What you’ll do: \n \n Own end-to-end technical delivery for cross-team initiatives—from problem framing and technical strategy through architecture, implementation, rollout, monitoring, and iteration.\n Set technical direction and execution plans in partnership with a Director and cross-functional leads, including defining milestones, sequencing, and quality bars for the domain.\n Build and evolve ML and GenAI systems that improve merchant quality and understanding (e.g., merchant content enrichment, attribute extraction/normalization, entity resolution, merchant/brand quality signals, and policy-aware transformations), with clear downstream impact on retrieval, ranking, and shopping surfaces.\n Establish robust evaluation and measurement practices across ML + LLM-assisted systems, including golden datasets, human-in-the-loop review loops, automated regression testing, offline/online metric alignment, and clear go/no-go launch criteria for quality, safety, and performance.\n Design systems with strong attention to quality, cost, latency, reliability, and safety, including guardrails, fallbacks, caching, and observability to support scaled production operations.\n Establish the ML engineering operating model for the org (where applicable): evaluation standards, launch readiness reviews, monitoring/alerting, and sustainable ownership practices to keep quality high as the roadmap scales.\n Partner with cross-functional stakeholders across Product, Engineering, Data Science, Design, Trust/Policy/Legal, and ML platform teams to align on goals, constraints, and rollout plans—and to turn ambiguous needs into concrete ML deliverables.\n Drive experimentation and iteration (A/B tests, holdouts), lead error analysis, and translate learnings into measurable improvements to user trust and shopping outcomes.\n Mentor and raise the bar for technical design, evaluation rigor, and production readiness across the team—enabling faster, safer iteration with AI/ML tooling and best practices.\n Help scale the domain by supporting hiring and onboarding over time (e.g., interview loops, onboarding plans, technical mentorship), as we build out ML engineering capacity.\n \n What we’re looking for: \n \n 8+ years of industry experience in ML engineering / applied ML / software engineering, including meaningful time operating as a Staff-level (or equivalent) IC delivering complex","salary_min":213180,"salary_max":314160,"location":"Toronto, Canada","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","llm","generative-ai","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8049465","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T19:43:15Z","expires_at":"2026-08-15T14:09:24.448775Z","created_at":"2026-07-09T14:08:40.616974Z","updated_at":"2026-07-16T14:09:24.569832Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/40013bc5-a45e-4719-a190-172312e50157"},{"id":"e5f37346-7318-4d03-bba6-98582a3995f9","company_id":"3d233526-89a8-48ea-b0ed-3304a35b8acf","title":"Senior Machine Learning Engineer, Health","slug":"senior-machine-learning-engineer-health-32b476c8","description":"WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives.\nThe Health team is responsible for developing novel algorithms and features that expand our health sensing capabilities. Our work spans several key areas, including women’s health, software as a medical device, wellness monitoring, longevity research, and emerging health insights. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members.\nAs a SeniorMachine Learning Engineer on our Health team, you will design, build, and productionize ML systems that deliver meaningful, personalized health metrics to millions of members. You will work at the intersection of data science, backend engineering, and cloud infrastructure—deploying robust, scalable, and reliable ML solutions built on physiological and behavioral data streams. This role emphasizes strong coding skills, system design, and the ability to deliver production-ready ML services.\n","salary_min":150000,"salary_max":210000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["healthcare","cloud","machine-learning","research"],"apply_url":"https://jobs.lever.co/whoop/d71ade70-3925-4dee-bf5c-8804b3897914/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-06T16:29:28.426Z","expires_at":"2026-08-15T14:17:40.907212Z","created_at":"2026-07-09T14:16:34.500559Z","updated_at":"2026-07-16T14:17:41.027765Z","company_name":"WHOOP","company_slug":"whoop","company_logo_url":"https://www.google.com/s2/favicons?domain=whoop.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e5f37346-7318-4d03-bba6-98582a3995f9"},{"id":"8fbc5e31-95be-46e8-bba3-be032a2b7ee7","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior / Staff Machine Learning Engineer, Applied AI","slug":"senior-staff-machine-learning-engineer-applied-ai-35ecb3ac","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking talented Senior/Staff Machine Learning Engineers with expertise in LLM training, evaluation, and production-oriented ML systems. You’ll work on improving Lila’s AI models for 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. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems.\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 experimentation, and ensure model behavior works well end to end inside the application.\n This role is ideal for someone who can bridge research and engineering: training or adapting models, building evaluation loops, debugging model behavior, and collaborating across AI and Software to move promising capabilities into production-quality systems.\n What You'll Be Building \n \n Close the last-mile gap between Lila AI model capabilities and customer-specific scientific workflows.\n Build evaluation loops that measure model quality, reliability, and customer fit.\n Design experiments to improve model performance across applied customer use cases.\n Feed customer learnings, data signals, and evaluation results back into the Lila AI model improvement cycles.\n Partner with AI researchers to translate model improvements into usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug model failures using traces, evaluations, customer context, and scientific feedback.\n Build reusable tooling for model adaptation, evaluation, and deployment workflows.\n \n What You'll Need to Succeed \n \n Strong experience building, training, adapting, or evaluating machine learning models.\n Strong software engineering skills in Python and modern ML frameworks such as PyTorch, JAX, or TensorFlow.\n Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray)\n Experience designing experiments, evaluation metrics, or test sets for model performance.\n Ability to debug model behavior using data, traces, logs, and qualitative feedback.\n Experience working across research and engineering teams to move ML capabilities into usable systems.\n Familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical model improvements.\n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows.\n Experience with scientific, technical, or data-intensive customer use cases.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Familiarity with retrieval-augmented generation, tool use, or agentic workflows.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience training MoE architectures.\n Experience working with product or customer-facing teams to translate needs into ML improvements.\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 — $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 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 A","salary_min":180000,"salary_max":336000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["rag","mlops","pytorch","reinforcement-learning","agents","llm","tensorflow","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4302917009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-01T17:14:20Z","expires_at":"2026-08-15T14:19:16.325938Z","created_at":"2026-07-03T14:17:38.345907Z","updated_at":"2026-07-16T14:19:16.456087Z","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/8fbc5e31-95be-46e8-bba3-be032a2b7ee7"},{"id":"680e18ad-093d-4ee4-9c8f-0111cb65eab2","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Staff Machine Learning Engineer, CustomerLake (ML/LLM)","slug":"staff-software-engineer-aiml-4e6c9e7f","description":"RDQ427R109 \n  \n At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best Data Intelligence Platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.\n  \n As one of the first engineers in the NYC Engineering office, you'll join a small, nimble team building new products from the ground up. We're building CustomerLake, the Customer Data Platform on Databricks, to bring enterprise-grade ML and AI personalization to every company whose data already lives on Databricks. The best B2C and B2B brands have historically relied on in-house ML/AI teams to power personalization, recommendations, churn and lifetime-value modeling, and audience targeting. Our goal is to deliver that same capability to companies that don't have an in-house team but already have their data in order on Databricks. This is a true 0-to-1 environment, combining the excitement of a startup with the resources of a tech leader like Databricks.\n  \n The impact you'll have: \n \n Evaluate ML and LLM approaches for CustomerLake's personalization use cases, push the models and algorithms forward, and continuously improve quality over time\n Go deep on how models behave in production: inspect individual traces, understand how the models reason, and tune and improve from there\n Build the platform and evaluation framework that let CustomerLake customers optimize for real business value such as purchases, retention, and product usage, not vanity metrics like email opens and clicks\n Push the team toward new directions and novel methods worth tackling, not just optimizing what already exists\n Partner closely with product management, engineering, and design to turn ambiguous customer problems into scalable, trustworthy solutions\n Set the technical foundation and best practices for our ML/AI personalization work as we grow this into several roles across our products over the next 1-2 years\n \n  \n What we look for: \n \n 10+ years of engineering experience, with a strong foundation across the full loop of shipping and improving ML/AI products\n Hands-on experience building and evaluating ML models and/or LLM systems for real product or business use cases; your understanding is practical, not purely academic, and you can make models work well inside a product\n Experience with personalization based on customer behavior (ideal) or transactions (acceptable), such as recommendations, targeting, churn, or lifetime-value modeling\n Proficiency in Python and modern ML frameworks (e.g., PyTorch), with hands-on experience in model evaluation and monitoring AI quality in production\n Familiarity with LLMs and generative AI, including techniques like retrieval-augmented generation (RAG), prompt design, fine-tuning, and evaluation\n A demonstrated product mindset, with the ability to translate ambiguous customer problems into scrappy MVPs and iterate quickly based on data and user feedback\n High ownership and bias for action in 0-to-1 environments: comfortable making pragmatic trade-offs, operating with incomplete information, and driving projects from idea through launch and adoption\n \n  \n Nice to have: \n \n Experience in martech, ideally a go-to-market or business use case with an analytical (rather than purely transactional) angle\n An academic or research background that can help us innovate and develop novel methods\n \n  \n  \n Pay Range Transparency \n Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . \n  \n Local Pay Range\n $192,000 — $260,000 USD \n About Databricks \n Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartere","salary_min":192000,"salary_max":260000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["rag","llm","generative-ai","fine-tuning","data-pipeline","pytorch","machine-learning"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8614863002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-30T23:49:00Z","expires_at":"2026-08-15T14:02:34.068577Z","created_at":"2026-07-01T14:02:14.272034Z","updated_at":"2026-07-16T14:02:34.184382Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/680e18ad-093d-4ee4-9c8f-0111cb65eab2"},{"id":"72eded0a-590c-41bc-be4e-f6c899cbdd03","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Applied Scientist / Machine Learning Engineer","slug":"applied-scientist-machine-learning-engineer-fa3d9046","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n Wayve is building embodied AI for the physical world, starting with autonomous driving. Instead of the hand-engineered, modular stacks that defined the first era of self-driving, we pioneered AV2.0: a single, end-to-end neural network that learns to drive from raw sensor data and generalises to new cities, vehicles, and conditions. Our foundation models, the GAIA family of generative world models and the LINGO family of vision-language-action models, let vehicles perceive, reason, and act in the open world. We have driven zero-shot across hundreds of cities on three continents, and we are now scaling from proving the science to deploying it with leading automakers and mobility partners, including Nissan, Stellantis, and Uber.\n The role \n This role sits in the AI Platform organisation, on the data flywheel that powers every model we ship. The thesis is simple and compounding: the more intelligently we curate, enrich, and evaluate the real-world driving experience our fleet generates, the faster our foundation models improve, and the further they generalise across geographies, embodiments, and OEM platforms. As deployment scales, the bottleneck is shifting from raw model capacity to the quality and intelligence of the data engine and the rigour of how we measure progress. That is the problem you will own.\n This is a dual-track role: we are hiring at either Applied Scientist or Machine Learning Engineer, at TC3 (Senior) or TC4 (Staff / Tech Lead), calibrated to your background. We are open on specialisation. There are three areas we are hiring into, and you can go deep in any one of them:\n \n Data curation : mine world-scale fleet data for the rare, long-tail, and safety-critical moments that move the model.\n Data enrichment : turn raw driving experience into high-signal training data through (semi-)automated enrichment, labeling, and data quality at scale.\n Foundation model evaluation : define how we know a driving foundation model is genuinely getting better, offline and in closed loop.\n \n Day to day, the role also spans the broader foundation-model stack, including vision-language-action and vision-language models for embodied AI, world modeling, policy learning, reinforcement learning, and reward modeling.\n Key responsibilities \n \n Mine world-scale fleet data for rare, long-tail, and safety-critical events using active learning, smart sampling, and embedding-based retrieval and dedup.\n Figure out what makes a good training dataset: which data, mix, and balance actually move the model, and turn that into repeatable curation across cities, sensor rigs, and embodiments.\n Build high-quality enrichments that teams across the company depend on, through (semi-)automated enrichment and labeling pipelines and data quality at scale.\n Build and fine-tune large-scale pretrained models, and run smaller-scale experiments to test and derisk ideas before committing serious compute.\n Help build the best embodied VLM / VLA in the world for driving (the LINGO line): push multimodal perception, reasoning, language, and action.\n Design rigorous offline and closed-loop evaluation: metrics and benchmarks that correlate with real on-road behaviour and safety, with deliberate coverage of rare and safety-critical scenarios.\n Use world-model-based evaluation (GAIA) to probe counterfactual “what if” scenarios safely, repeatably, and at scale.\n Contribute across the wider foundation-model stack as the work demands: generative world models (GAIA), policy learning, reinforcement learning, and reward modeling.\n \n About you \n Essential \n \n A Masters with around 6 or more years of relevant experience, or a PhD with 2 or more years, in computer science, machine learning, robotics, mathematics, or a related field (required).\n Strong ML and software fundamentals, and a track record of taking ML from research into production systems that run at scale.\n Hands-on strength in one or more of: data curation, foundation model training, large-sc","salary_min":311850,"salary_max":370000,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","reinforcement-learning","autonomous-vehicles","pytorch","deep-learning","llm","robotics","machine-learning"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8614636002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-30T21:48:01Z","expires_at":"2026-08-15T14:13:56.379501Z","created_at":"2026-07-01T14:12:53.850528Z","updated_at":"2026-07-16T14:13:56.573514Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/72eded0a-590c-41bc-be4e-f6c899cbdd03"},{"id":"6264b5d2-3587-465d-a718-1ad517b5877f","company_id":"9bad7e3a-74e6-4dae-87c5-f3e9f0e72bd0","title":"Senior/Staff Software Engineer - Machine Learning \u0026 System Optimization","slug":"seniorstaff-software-engineer-machine-learning-system-optimization-4d5915e4","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 Machine Learning and System Optimization Engineer, you will orchestrate and allocate overall system capacity to various core perception models running on-bot, as well as drive large initiatives that allow for more efficient inference by sharing various parts of the perception stack with one another.\nYou 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 compressing, accelerating, and deploying complex models, including LLMs, VLMs, or foundation models, for power- and thermal-constrained vehicle SoCs.\nIn addition, you will optimize ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices.\n","salary_min":226000,"salary_max":307000,"location":"Foster City, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","llm","gpu","machine-learning"],"apply_url":"https://jobs.lever.co/zoox/e1e3ddc4-b6cc-4873-87cf-bff2295109fb/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-30T21:45:57.955Z","expires_at":"2026-08-15T14:06:35.441206Z","created_at":"2026-07-01T14:05:46.847139Z","updated_at":"2026-07-16T14:06:35.558748Z","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/6264b5d2-3587-465d-a718-1ad517b5877f"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":580,"total_pages":29}