{"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","gpu","code-generation","distributed-systems","search","jax","fine-tuning","llm"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-06-29T14:00:21.52187Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-05-30T14:00:21.633054Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"530f705a-007a-497f-9f62-9a6e196ea9ad","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-e1742de5","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is a remote position based in Toronto, Canada. \n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $210,070 to $282,851 (CAD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and accountability, we enable trust and autonomy—leaders take accountability for decisive action, and teams own outcomes ","salary_min":210070,"salary_max":282851,"location":"Toronto, Canada","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","agents","mlops","gpu","healthcare","deep-learning","pytorch","llm"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961536","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:14Z","expires_at":"2026-06-29T14:07:04.607932Z","created_at":"2026-05-30T14:07:04.722791Z","updated_at":"2026-05-30T14:07:04.722791Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/530f705a-007a-497f-9f62-9a6e196ea9ad"},{"id":"58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-288c8ba8","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is an office-based, hybrid position at our US headquarters located in Salt Lake City, Utah . Employees are expected to work in the office at least 50% of the time.\n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $151,130 to $203,490 (USD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and acco","salary_min":151130,"salary_max":203490,"location":"Salt Lake City, Utah","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pytorch","deep-learning","cloud","mlops","gpu","llm","distributed-systems","healthcare"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961460","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:13Z","expires_at":"2026-06-29T14:07:04.532978Z","created_at":"2026-05-30T14:07:04.642889Z","updated_at":"2026-05-30T14:07:04.642889Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9"},{"id":"a944334e-23f0-4033-b1c8-307c9e7c7124","company_id":"75dcf7c0-5121-45f1-8d1b-6bfbfe15072f","title":"Helix AI Engineer, Backend Infrastructure ","slug":"helix-ai-engineer-backend-infrastructure-13269072","description":"Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose and require 5 days/week in-office collaboration. It’s time to build.\n We're looking for a senior-level backend engineer who has scaled high-throughput, low-latency data systems and has strong instincts around cloud infrastructure and real-time streaming pipelines. You'll architect and build the core backend systems that power Figure's real-time data infrastructure — enabling the scale and reliability that our AI and robotics platforms depend on.\n This is a high-ownership role at the intersection of media and sensor data streaming, cloud systems, and applied ML serving. You'll work closely with our AI and robotics teams to ensure latency, reliability, and throughput meet the demands of real-world robot operation.\n WHAT YOU'LL DO \n \n Architect and scale cloud backend infrastructure for high-concurrency, real-time streaming of media and sensor data across robot fleets and user sessions.\n Design and build low-latency data pipelines that ingest, route, and process high-bandwidth streams — including camera feeds, IMU data, and other robot sensor outputs — into our AI stack in real time.\n Own reliability, latency, and throughput SLAs for streaming and data infrastructure.\n Collaborate with AI and robotics teams to integrate ML model serving into real-time data pipelines.\n Build observability, alerting, and tooling to give the team full situational awareness over live robot traffic.\n Drive architectural decisions and mentor engineers across the team.\n \n WHAT WE'RE LOOKING FOR \n \n Deep experience scaling cloud backend systems handling high-concurrency, real-time data streams — media, sensor, telemetry, or equivalent high-bandwidth pipelines.\n Strong fundamentals in distributed systems: stream processing, connection management, data transport, and low-latency architecture.\n Proficiency in one or more backend languages (Go, C++, Python, Rust) and cloud platforms (AWS, GCP, or Azure).\n Experience with containerized infrastructure, service mesh, and large-scale deployment pipelines.\n Strong communication and cross-functional collaboration skills.\n \n NICE TO HAVE \n \n Hands-on experience integrating AI inference serving (Triton Inference Server, TensorRT, SageMaker, or similar) into real-time data pipelines.\n Background in robotics, autonomous vehicles, live media platforms, or other latency-critical streaming domains.\n Familiarity with protocols such as WebRTC, RTSP, gRPC, or Kafka for real-time data transport.\n Experience with on-device or edge inference and the tradeoffs of cloud vs. edge processing.\n \n The US base salary range for this full-time position is between $150,000 - $400,000 annually.\n The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.","salary_min":150000,"salary_max":400000,"location":"San Jose, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["robotics","mlops","api-design","autonomous-vehicles","cloud","gpu","data-pipeline","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/figureai/jobs/4685172006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T21:42:25Z","expires_at":"2026-06-29T14:05:53.514412Z","created_at":"2026-05-29T14:18:08.491663Z","updated_at":"2026-05-30T14:05:53.629497Z","company_name":"Figure AI","company_slug":"figure-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=figure.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a944334e-23f0-4033-b1c8-307c9e7c7124"},{"id":"6dc81f39-064c-435f-95c1-b6c70be6a1c5","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Machine Learning Engineer, App SW","slug":"machine-learning-engineer-app-sw-73eaf56f","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 The Role  \n As an  ML Engineer  within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalisation, comfort, and collaboration.\n You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.\n Responsibilities:\n \n Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.\n Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.\n Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.\n Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.\n Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.\n Collaborate cross-functionally across various teams to ensure integration and iteration velocity.\n Mentor senior engineers and shape the long-term technical direction across Autonomy.\n \n About you:  \n In order to set you up for success as a Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n Essential\n \n Extensive and proven track record of shipping deep learning systems to production.\n Expert in deep learning (esp. sequential models, control, planning, or perception).\n Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.\n Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.\n Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.\n \n Desirable\n \n Prior work in autonomous driving, imitation learning, or trajectory prediction.\n Familiarity with personalization, human behavior modeling, or driver intent inference.\n Experience integrating ML systems into production hardware or multi-agent simulation.\n \n This role is a full-time role based in Sunnyvale or Detroit (hybrid) and the reasonably estimated salary for this role ranges from $283,500 to $381,600, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.\n At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.  \n #LI-KM1 \n Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. \n We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition  (including breastfeeding) or any other ba","salary_min":283500,"salary_max":381600,"location":"Detroit","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["autonomous-vehicles","data-pipeline","robotics","pytorch","generative-ai","deep-learning","gpu","agents"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8568694002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T14:51:17Z","expires_at":"2026-06-29T14:12:44.75073Z","created_at":"2026-05-29T14:50:38.39389Z","updated_at":"2026-05-30T14:12:44.866872Z","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/6dc81f39-064c-435f-95c1-b6c70be6a1c5"},{"id":"df157081-449b-4687-984b-55a9b56309e6","company_id":"0d5cf132-03f2-47aa-8161-dcb1b95aca68","title":"Runtime Engineer","slug":"runtime-engineer-b8e275b2","description":"What MatX is Building \n MatX is building custom silicon for large-language-model inference and training, with HW/SW co-design across ISA, RTL, simulator, compiler, and kernels so each layer benefits from the others. The runtime owns the host-side stack and the contracts that bind those teams together.\n What You'll Do Here \n \n Build the host-side interface library — device memory management, DMA, streams and events, sync primitives — that every compiler-emitted program runs on top of\n Own and extend the executable format: the compiler→runtime contract, its versioning, the weight and quantization layouts that let compiler and runtime evolve independently\n Design the custom-kernel ABI — calling convention, sync semantics, lifecycle — and the host-side marshaling layer (DLPack, the buffer protocol, numpy) that gets Python tensors to the device\n Build Python bindings via PyO3, with a C-ABI shim as the alternative integration path for downstream consumers\n Build the LLM inference serving stack — paged KV cache, continuous batching, request scheduling, token streaming — and the cluster orchestration primitives underneath it\n Bring up interconnect topology from the host and own the failure-detection and clean-teardown path for stop-restructure-resume recovery across racks\n Design what the chip exposes to host-side profilers and debuggers — perf counters, traces, and the Python surfaces ML engineers actually use — and hit measurable performance targets on runtime overhead and serving throughput\n \n Who You Are \n \n Strong experience in a systems programming language — Rust, C, C++, or Go — including memory management, allocator design, and FFI/ABI work\n Have built Python interop layers in production (PyO3, ctypes, pybind11, or equivalent C-ABI bridging)\n Have designed and maintained API or ABI contracts between teams — versioning, evolution, breaking-change discipline — not just consumed someone else's\n Hands-on with at least one accelerator programming model (CUDA, ROCm, oneAPI Level Zero, TPU, or comparable) — enough to reason about device memory, async execution, and kernel launch\n ML-systems literate — comfortable with the training and inference loop, what collectives do, what a tensor layout is. Research depth not required.\n \n Bonus Points If You Have \n \n LLM inference internals — vLLM, TensorRT-LLM, or SGLang (paged attention, scheduler design)\n Rust at depth, including proc macros, unsafe with soundness reasoning, and complex lifetime/trait work\n Custom allocator design (slab, paged, arena) or other low-level memory work\n ML framework integration experience (PyTorch custom backends, JAX/XLA, ONNX runtime)\n Profiler or tracing infrastructure work (perfetto, Nsight, or a custom stack)\n Driver-adjacent or kernel-bypass work, or prior new-silicon bring-up\n \n Compensation \n The US base salary for this full-time position is determined based on a variety of factors including role, experience, location, job related skills, and relevant education and training. Career length is only a guideline for compensation.\n \n Early Career - $120,000 - $250,000 + equity\n Mid Career - $175,000 - $362,500 + equity\n Senior Career - $250,000 - $475,000 + equity\n \n What We Offer \n \n A Stake in our success  Generous equity, with option cash/equity swap at offer, and option to employee early exercise.\n Health \u0026 Wellness  Company subsidized Health, Dental, Vision, and Life insurance; Pre-tax Health Savings Accounts with generous company contribution (even if you don’t)\n Time To Recharge  4 weeks paid time off (accrued), 12 company holidays, and 3 weeks remote/flexible work per year\n Support to Parents  Up to 12 weeks of paid parental leave, regardless of your path to parenthood\n Learning \u0026 Development  $1,500 yearly towards your professional development e.g. conferences, courses, and other learning opportunities\n Team Connection  Team Lunches, quarterly off-sites, and regular town halls\n Financial Wellbeing.  401K and/or Roth IRA, with 5% company contribution, even if you don’t!\n Flexible Spending Accounts  Pre-tax spend accounts for medical, dental/vision, dependent care, parking, and transit expenses\n Commute On Us  For those commuting up to 1 hour, put your rideshare cost on our company card and reclaim the drive-time to get work done!\n MatX E[x]tras  $50 per month to use on the perks you care about most \n Remote Perks  We work remotely Monday \u0026 Friday, supported by home-tech setup, and remote wifi expense reimbursement\n \n As part of our dedication to the diversity of our team and our focus on creating an inviting and inclusive work experience, MatX is committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin or ancestry, sex, gender, gender identity, gender expression, sexual orientation, age, physical or mental disability, medical condition, marital/domestic partner status,","salary_min":250000,"salary_max":475000,"location":"Mountain View, CA","workplace":"remote","job_type":"full-time","experience_level":"principal","tags":["gpu","pytorch","cloud","llm"],"apply_url":"https://job-boards.greenhouse.io/matx/jobs/5231974008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T03:06:08Z","expires_at":"2026-06-29T14:13:28.359183Z","created_at":"2026-05-27T14:14:01.932882Z","updated_at":"2026-05-30T14:13:28.471451Z","company_name":"MatX","company_slug":"matx","company_logo_url":"https://www.google.com/s2/favicons?domain=matx.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/df157081-449b-4687-984b-55a9b56309e6"},{"id":"1d143344-1df1-4f1b-9d3d-471e99c3eb21","company_id":"baca6349-80b0-417a-97a1-b31511860322","title":"Site Reliability Engineer","slug":"site-reliability-engineer-0d3e749c","description":"Runpod is the foundational platform for developers to build and run custom AI systems that scale. With over 500,000 developers worldwide and an annual recurring revenue run rate exceeding $120M, Runpod operates at the intersection of developer velocity and production-scale AI. Founded in 2022, we’ve grown rapidly by building infrastructure purpose-built for modern AI workloads. Our platform enables teams to move from experimentation to deployment with flexibility across cloud, on-prem, and hybrid environments. As a remote-first, globally distributed company, we are building the infrastructure layer that powers the next generation of AI systems.\n The Reliability team owns the availability, performance, and operational excellence of Runpod’s global platform. While infrastructure teams build the systems, the Reliability team ensures those systems remain resilient, observable, and scalable under real-world production conditions.\n This team is responsible for:\n \n Defining and enforcing reliability standards across engineering\n Designing incident response processes and improving recovery times\n Building observability systems and reliability tooling\n Driving SLO adoption and production readiness reviews\n \n Reducing operational toil through automation\n The Reliability team works cross-functionally with Infrastructure, Product Engineering, and Support to ensure our systems remain stable and performant as we scale rapidly. We value proactive problem solving, automation-first thinking, and strong ownership of production systems.\n As a Site Reliability Engineer on the Reliability team, you will focus on ensuring the stability and resilience of Runpod’s distributed platform. You will partner with engineering teams to improve system design, strengthen observability, and prevent incidents before they happen.\n This role blends software engineering with production operations. You’ll work on reliability frameworks, SLO design, automation, and production hardening, reducing errors and improving performance across different services and infrastructure.\n This is a high-impact role central to maintaining trust with developers running critical AI workloads on Runpod.\n Your Impact \n \n Increase platform uptime and reduce incident frequency and duration\n Establish and operationalize SLIs/SLOs across services\n Improve MTTR through better tooling, automation, and runbooks\n Strengthen production readiness standards\n Drive long-term systemic reliability improvements\n \n You will influence how reliability is defined and measured across Runpod and help build the operational backbone of the company.\n Responsibilities: \n Reliability Engineering \n \n Define and implement SLIs/SLOs for critical services\n Lead incident response and coordinate cross-team mitigation efforts\n Conduct blameless postmortems and ensure corrective actions are completed\n Perform production readiness reviews for new services and features\n Identify systemic risks and drive preventative improvements\n \n Observability \u0026 Monitoring \n \n Design and improve monitoring, alerting, and dashboards (Prometheus, Grafana, etc.)\n Improve signal-to-noise ratio in alerts and reduce alert fatigue\n Build internal tooling for reliability tracking and reporting\n Improve visibility into GPU performance and distributed systems health\n \n Automation \u0026 Toil Reduction \n \n Automate recurring operational workflows\n Build tools and scripts (Python, Go, Bash) to eliminate manual processes\n Improve deployment safety through automation and guardrails\n Strengthen CI/CD reliability and release processes\n \n Cross-Functional Reliability Advocacy \n \n Partner with engineering teams to improve system resilience\n Provide guidance on fault tolerance, scalability, and failure handling\n Contribute to architectural discussions with a reliability-first mindset\n \n Requirements: \n \n 5+ years of experience in SRE, Reliability Engineering, or Production Engineering\n Strong Linux systems and Networking expertise\n Experience managing containerized production systems\n Strong understanding of distributed systems and failure modes\n Experience defining and managing SLIs/SLOs\n Proven incident response and postmortem leadership experience\n Strong scripting or programming skills\n Experience with monitoring and alerting systems\n Excellent written communication skills\n Successful completion of a background check\n \n Preferred: \n \n Experience with GPU infrastructure or AI/ML platforms\n Experience improving reliability in high-growth or large scale environments\n Familiarity with GPU observability tooling\n Experience with Infrastructure as Code\n Experience working in startup environments\n Experience building internal reliability platforms or frameworks\n \n What You’ll Receive: \n \n The competitive base pay for this position ranges from $150,000- $200,000 usd. This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candida","salary_min":150000,"salary_max":200000,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["gpu","distributed-systems","infrastructure","devops"],"apply_url":"https://job-boards.greenhouse.io/runpod/jobs/5229443008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T19:03:55Z","expires_at":"2026-06-29T14:13:40.120853Z","created_at":"2026-05-27T14:14:13.643293Z","updated_at":"2026-05-30T14:13:40.241741Z","company_name":"RunPod","company_slug":"runpod","company_logo_url":"https://www.google.com/s2/favicons?domain=runpod.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1d143344-1df1-4f1b-9d3d-471e99c3eb21"},{"id":"1e411b32-416c-4234-bcb3-3604b204f141","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Staff Machine Learning Engineer, AV Core","slug":"staff-machine-learning-engineer-av-core-1f2ae697","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 The role  \n As a Staff Machine Learning Engineer on Wayve’s Core Model Safety team in AV Core, you will help shape what our end-to-end driving model must understand to be safe and reliable in the real world - and turn that into trained capabilities, clear evidence, and adoption on the shared backbone across core and product engineering.\n  \n The Core Model Safety team builds foundational capabilities for assisted and automated driving - collision avoidance, scene understanding, model understanding, and robustness under failure. You will work in a focused, high-impact senior team with strong ownership, access to large-scale training and fleet data, and close partners in research, simulation, evaluation, and applied engineering.\n  \n Key responsibilities \n \n Drive Core Model Safety roadmap themes owning the full lifecycle from research to offline/online experiments to technology transfer.\n Train and deploy end-to-end AV 2.0 models on our global fleet, using large-scale, diverse data to validate capabilities and improve generalisation across vehicles, markets, and driving conditions.\n Build high-value open-loop and closed-loop evaluations for core capabilities and representation learning.\n Align priorities and learn from the organisation - with AV Core, Evaluation, and Product Engineering on roadmaps and failure modes; from fleet, simulation, and product feedback; and through mentoring others on the team.\n Maintain awareness of the wider business context - division and company priorities, near-term product programmes, and how Core Model Safety work enables them.\n \n About you   \n In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n  \n Essential  \n \n 5+ years in ML engineering, including pathfinding in ambiguous problems - from scoping and evals to establishing a direction (and knowledge transfer) for others to build on.\n Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.\n Hands-on experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent.\n Hands-on experience training shared representations with multiple tasks or objectives (multi-stage or joint training), including real trade-offs across data and losses.\n Staff-level technical leadership: research-literate and pragmatic, setting direction, raising the bar, and leading cross-functional work without formal line management.\n \n  \n Desirable  \n \n Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems.\n Experience in 3D scene understanding and representation learning for geometric and semantic perception, large-scale semantic enrichments.\n Experience in reward modelling, behaviour modelling, model introspection, and/or interpretability.\n Experience with redundant or fallback architectures, safety-critical systems.\n Experience across foundations/pretraining and applied engineering teams; large-scale training infrastructure and/or agentic workflows.\n \n This is a full-time role based in our office in Sunnyvale.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. The reasonably estimated salary for this role ranges from $336,400 to $370,300, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.\n Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. \n We understand that everyone has a unique set of skills and experiences and that no","salary_min":336400,"salary_max":370300,"location":"Sunnyvale, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["generative-ai","autonomous-vehicles","agents","robotics","pytorch","reinforcement-learning","gpu","pre-training"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8562545002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T19:02:03Z","expires_at":"2026-06-29T14:12:48.484991Z","created_at":"2026-05-27T14:13:12.451192Z","updated_at":"2026-05-30T14:12:48.600908Z","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/1e411b32-416c-4234-bcb3-3604b204f141"},{"id":"789228a9-72bb-4a56-98ca-87b1968a76fd","company_id":"698abc6f-9497-4ea6-809f-f0f7c2788a46","title":"Staff GPU Systems Engineer, Space Computing","slug":"staff-gpu-systems-engineer-space-computing-bf7c9dd7","description":"At Relativity Space, we’re building rockets to serve today’s needs and tomorrow’s breakthroughs. Our Terran R vehicle will deliver customer payloads to orbit, meeting the growing demand for launch capacity. But that’s just the start. Achieving commercial success with Terran R will unlock new opportunities to advance science, exploration, and innovation, pioneering progress that reaches beyond the known. \n Joining Relativity means becoming part of something where autonomy, ownership, and impact exist at every level. Here, you're not just executing tasks; you're solving problems that haven’t been solved before, helping develop a rocket, a factory, and a business from the ground up. Whether you’re in propulsion, manufacturing, software, avionics, or a corporate function, you’ll collaborate across teams, shape decisions, and see your work come to life in record time. Relativity is a place where creativity and technical rigor go hand in hand, and your voice will help define the stories we’re writing together. Now is a unique moment in time where it’s early enough to leave your mark on the product, the process, and the culture, but far enough along that Terran R is tangible and picking up momentum. The most meaningful work of your career is waiting. Join us. \n  \n About the Team:  \n The Interplanetary Sciences Program was established  to expand access to scientific exploration across our solar system. Its mission is to make planetary research faster, more affordable, and more capable than ever before by rethinking how science missions are designed, built, and  operated . The program aims to enable scientists to send instruments to distant worlds without decades of development or prohibitive costs. By creating a sustainable model for interplanetary exploration, we are transforming space science from an occasional event into a continuous process of discovery that accelerates knowledge, broadens participation, and inspires the next generation of explorers.   \n About the Role: \n \n Own the GPU compute environment for a space-based data center — setup, driver integration, container runtime, job scheduling, and performance optimization — building the platform that enables onboard AI/ML inference and SAR reprocessing millions of miles from the nearest sysadmin \n Profile and optimize compute performance across the full stack: GPU utilization, memory bandwidth, I/O throughput, and storage interface performance, squeezing maximum science return from constrained power and thermal budgets that shift between sunlit burst processing and eclipse idle periods \n Build power and thermal-aware compute scheduling that orchestrates batch workloads around orbital constraints, coordinating with the storage platform to sustain 10 Gbps data movement between NAS and compute nodes during processing windows \n Develop compute health monitoring and upset recovery mechanisms — checkpoint/restart strategies, GPU fault detection, and automated recovery — so a radiation-induced upset means a restarted job, not a lost processing window \n Integrate GPU drivers with the payload Linux image in coordination with the Platform RE, manage the container runtime for compute workloads, and ensure the platform reliably runs ML frameworks and SAR processing pipelines maintained by the broader operations team \n \n About You: \n \n BS/MS in Computer Science or Electrical Engineering and 5+ years of relevant experience \n Hands-on experience with GPU programming and compute frameworks — CUDA, ROCm, or OpenCL — with real performance profiling and optimization work, not just running tutorials \n Strong Linux systems administration and performance tuning skills: you've diagnosed I/O bottlenecks, tuned memory management, and understood why a workload isn't hitting expected throughput \n Experience with container technologies (Docker, Podman, or lightweight alternatives) and HPC job scheduling concepts \n Working proficiency in Python for tooling, scripting, and ML framework integration, with C/C++ skills for performance-critical system components \n \n Nice to haves but not required:    \n \n Experience with HPC cluster administration, ML infrastructure, or cloud GPU compute platforms at scale \n Deep familiarity with ML framework runtime requirements — PyTorch or TensorFlow deployment, model serving, and inference optimization \n Knowledge of GPU compute architectures at the hardware level: CUDA cores, compute units, memory hierarchies, and how they affect real workload performance \n Experience with high-throughput data movement and storage I/O optimization — NFS tuning, buffer management, and sustaining multi-gigabit throughput \n Background in power-managed computing: duty cycling, thermal throttling, and workload scheduling under variable power constraints \n Experience designing checkpoint/restart or fault-tolerant batch processing systems — space experience not required, similar problems exist in large-scale distributed infrast","salary_min":181000,"salary_max":248500,"location":"Long Beach, California","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["mlops","pytorch","fine-tuning","gpu","tensorflow"],"apply_url":"https://boards.greenhouse.io/relativity/jobs/8560518002?gh_jid=8560518002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T17:01:37Z","expires_at":"2026-06-29T14:18:13.388673Z","created_at":"2026-05-27T14:19:05.177476Z","updated_at":"2026-05-30T14:18:13.506801Z","company_name":"Relativity","company_slug":"relativity","company_logo_url":"https://www.google.com/s2/favicons?domain=relativity.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/789228a9-72bb-4a56-98ca-87b1968a76fd"},{"id":"de8c4702-b4e3-447a-84b5-b971e4cc8e4d","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Lead Software Architect","slug":"lead-software-architect-fc53e5cc","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n ABOUT THE TEAM\n At Anduril, our Battelspace Awareness Command and Control Software team specializes in solving complex, real-world problems through cutting-edge algorithms and intelligent software integrations. Operating in small, innovative teams, we push the boundaries of what's possible to deliver advanced technologies with mission-critical applications. Our commitment doesn't end with academic research or proof-of-concept experiments; we measure our success by the real-world impact of our deployed solutions. \n  \n ABOUT THE JOB \n We are looking for a Lead Software Architect to join our rapidly growing C2 Systems team in  Broomfield, CO.   In this role, you will be responsible for defining the architectural direction of large-scale, real-time, mission-critical C2 software that powers Anduril's air and missile defense and battlespace awareness capabilities. You will lead the design of high-performance systems spanning tactical edge deployments to distributed backend infrastructure, make critical trade-offs between performance, modularity, and maintainability at scale, and mentor senior engineers across the C2 organization . This will require deep expertise in  modern C++ and Rust , experience architecting numerics-heavy real-time systems, fluency with asynchronous/multithreaded programming (e.g., Tokio), and a strong grasp of the complex intersection of software and math — including state estimation, target tracking, optimization, and dynamic programming (MCP / Bellman equations) . If you are someone who thrives on ambitious defense problems, enjoys troubleshooting real-world systems where issues could range from electromagnetics to faulty bit encodings to incorrect math assumptions, and wants to build software that is beyond a proof-of-concept — part of real tactical code deployed to the warfighter — then this role is for you\n WHAT YOU’LL DO\n \n Define and drive the architectural vision for large-scale C2 software systems that ingest, fuse, and act on data from diverse sensors in real time\n Lead the design and implementation of performant, real-time, numerics-heavy algorithms in Rust and/or C++ at production scale\n Partner closely with research scientists to transition advanced algorithms (target tracking, state estimation, sensor-effector pairing, asset scheduling) from prototype into tactical, deployed code\n Make high-leverage architectural trade-offs across performance, modularity, testability, and maintainability for mission-critical, edge-deployed systems\n Mentor and technically lead senior engineers, setting coding standards, review processes, and CI/CD best practices across the team\n Engage directly with customers (DoD agencies, Army, Air Force, MDA, SCO) to ensure successful outcomes for mission-critical needs\n Troubleshoot complex, real-world system issues spanning software, math assumptions, sensor behavior, and networking\n Contribute to all phases of the software development lifecycle including prototyping, modeling \u0026 simulation, field testing, and deployment\n Help shape hiring and technical growth of the broader C2 organization as it hyper scales\n \n REQUIRED QUALIFICATIONS\n \n 10+ years of software engineering experience with a Bachelor's degree (or equivalent) in Computer Science, Applied/Computational Mathematics, Electrical Engineering, Aerospace Engineering, Controls/Dynamical Systems, Statistics, or related field\n Expert-level proficiency in modern C++ and/or Rust, including asynchronous and multithreaded programming (e.g., Tokio for Rust)\n Proven experience architecting large-scale, production-grade codebases in real-time or high-performance environments\n Deep experience writing performant, real-time software with numerics-heavy algorithms\n Strong foundation in applied mathematics: probability theory, linear algebra, optimization, differential equations (ODEs), and statistics\n Experience with CI/CD, unit testing, git version control, and microservices\n Eligible to obtain and maintain an active U.S. Secret security clearance\n \n PREFERRED QUALIFICATIONS\n \n M.S. or Ph.D. in a technical field (dual academic background in software + math highly valued)\n Domain expertise in target tracking, state estimation, Kalman filters, sensor fusion, or signal processing \n Prio","salary_min":219000,"salary_max":290000,"location":"Broomfield, CO","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["microservices","gpu","payments","computer-vision","cloud","llm"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5124653007?gh_jid=5124653007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-20T16:37:55Z","expires_at":"2026-06-29T14:06:43.193134Z","created_at":"2026-05-27T14:07:02.654312Z","updated_at":"2026-05-30T14:06:43.308809Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/de8c4702-b4e3-447a-84b5-b971e4cc8e4d"},{"id":"bc38cbd7-6147-49eb-a610-64fb031af669","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Staff Machine Learning Engineer, Voice AI ","slug":"staff-machine-learning-engineer-voice-ai-049973bf","description":"About the Role \n Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability.\n We're looking for a Staff ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly.\n This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech.\n \n Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech.\n Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference.\n Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure.\n Build quality evaluation frameworks that guide model selection for customers and inform the roadmap.\n Join a small, early-stage team with outsized impact on a fast-growing product area.\n \n  \n Responsibilities \n \n Own the voice inference roadmap end-to-end — define and execute the technical strategy for optimizing STT, TTS, and speech-to-speech models across Together's infrastructure, with a clear-eyed view of where the field is heading and how to position the platform ahead of it.\n Drive best-in-class inference performance — architect and implement systems targeting leading TTFB, throughput, and GPU utilization for voice workloads; set the performance bar others in the industry measure against, not just catch up to.\n Lead productionization of voice models at scale — design the serving architecture for serverless and dedicated endpoints, including batching strategies, streaming inference pipelines, and memory management tailored to real-time audio; own reliability and latency SLAs.\n Build the voice evaluation platform — design a rigorous, extensible evaluation framework covering WER across accents, languages, and noise conditions for STT; naturalness, latency, and pronunciation fidelity for TTS; establish the internal benchmark methodology that informs model selection and roadmap decisions.\n Shape the architecture for next-generation model support — anticipate and enable emerging model paradigms — audio-native LLMs, codec-based architectures (SNAC, Encodec), and end-to-end speech-to-speech systems — before they're mainstream, not after.\n Serve as the technical DRI for model partner integrations — lead deep collaboration with partners such as Cartesia, Deepgram, and Rime; own the full lifecycle from integration to optimization to ongoing performance accountability.\n Diagnose and resolve the hardest performance problems in the stack — conduct systematic profiling and root-cause analysis from GPU kernel behavior to framework-level bottlenecks; drive shipped improvements with documented, measurable impact.\n Influence platform architecture across the organization — partner with platform engineering leadership to ensure the serving layer is built for the latency and reliability demands of real-time voice APIs; your technical decisions should raise the ceiling for the whole team.\n Define and scale voice fine-tuning capabilities — lead the technical direction for enabling customers to fine-tune STT and TTS models on Together's infrastructure, establishing the primitives for differentiated voice experiences.\n Lay technical foundations for a category-defining product surface — architect systems with enough foresight that they support multiple new voice products with minimal rework; think in terms of platforms, not point solutions.\n \n Requirements \n \n 8+ years of ML engineering experience, with a demonstrated focus on model serving, inference optimization, or ML infrastructure at production scale — including systems you've owned from design through live traffic.\n Deep, practical expertise in LLM serving engines (vLLM, SGLang, TensorRT-LLM, or equivalent) — you've modified engine internals, debugged edge cases under load, and contributed improvements back; you don't stop at the API surface.\n Expert-level Python and PyTorch proficiency, with a strong command of GPU optimization — CUDA kernels, memory hierarchies, profiling toolchains — and a track record of turning that knowledge into shipped latency or throughput wins.\n Proven system design judgment — you've made arch","salary_min":220000,"salary_max":280000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["mlops","gpu","llm","pytorch","fine-tuning","speech","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5140763007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T18:19:46Z","expires_at":"2026-06-29T14:01:50.400776Z","created_at":"2026-05-27T14:02:00.695384Z","updated_at":"2026-05-30T14:01:50.521421Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bc38cbd7-6147-49eb-a610-64fb031af669"},{"id":"93fe1130-903b-482a-9d72-9c439f5cead5","company_id":"0d5cf132-03f2-47aa-8161-dcb1b95aca68","title":"Software Product Manager","slug":"software-product-manager-96ae9837","description":"What MatX Is Building \n MatX is on a mission to make the world's best AI models run as efficiently as possible, accelerating global progress in AI quality and accessibility. We are building cutting-edge AI infrastructure at the intersection of hardware and software, and our team is growing quickly.  \n This is MatX's first software-focused Software Product Manager. You'll start by doing mostly internal work that sits at the boundary of product management and technical program management: mapping interfaces between sub-teams, driving API boundary decisions, and making sure the compiler, simulator, kernels, and runtime teams are building toward a coherent whole. You will be managing risks and mitigations applying structural problem solving methodology as needed. You'll also be shaping the software side of our product definition — what goes into the SDK, what tools and documentation we ship, and what kernels and systems software we build or expose to partners.  \n What You'll Do Here \n \n Partner Management: Own the SDK partner relationship model — which partners get early access, what commitments we make, and how we collect and triage feedback \n API \u0026 Systems Definition: Define the shape of the MatX SDK and the systems software surface area we ship to customers: runtime APIs, profiling tools, compiler interfaces, and simulator integrations Own and document API boundaries between the compiler, simulator, kernels, and runtime sub-teams, and across hardware, architecture, and systems software — driving alignment on interface contracts, timelines, and dependencies \n \n \n Kernel Strategy: Determine what kernels MatX should author versus expose for customers to write themselves, and define the tools and documentation that make customer kernel authorship tractable \n Technical Translation: Translate between customer needs and engineering constraints, and contribute to roadmap prioritization across the software stack with a clear view of customer impact and strategic fit \n \n Who You Are \n \n Experience in product management or technical program management, working directly on systems software, compilers, ML frameworks, or developer-facing SDKs \n Strong enough technical depth to read a Rust API, understand what a compiler IR is, and have a meaningful conversation with an engineer about simulator fidelity tradeoffs — you don't need to write production code, but you need to earn trust with people who do  \n Experience defining and documenting API contracts or SDK specifications in a technical organization  \n Comfort operating in ambiguity and doing the unglamorous internal coordination work before the external product work becomes available  \n Strong written communication — you'll write a lot of internal specs, interface docs, and partner-facing materials  \n \n Bonus Points If You Have \n \n Background in ML accelerator software (inference runtimes, kernel libraries, compiler backends, or ML frameworks such as PyTorch, JAX, or TensorFlow at the systems level) \n Experience working with early SDK partners or developer ecosystem programs at a hardware or developer-tools company  \n Familiarity with CUDA/HIP, MLIR, LLVM, or similar low-level toolchains — not necessarily as an author, but as someone who has shipped documentation or tooling around them \n Previous experience at a company that shipped novel hardware with a co-designed software stack (GPU vendors, custom accelerator startups, or similar)  \n Previously a software engineer \n \n Compensation \n The US base salary for this full-time position is determined based on a variety of factors including role, experience, location, job related skills, and relevant education and training. Career length is only a guideline for compensation.  \n \n Early Career - $120,000 - $275,000 + equity \n Mid Career - $175,000 - $400,000 + equity \n Senior Career - $250,000 - $600,000 + equity \n \n What We Offer \n \n A Stake in our success  A cash/equity mix that fits your needs and option to do early exercise \n Health \u0026 Wellness  Company subsidized Health, Dental, Vision, and Life insurance; Pre-tax Health Savings Accounts with generous company contribution (even if you don’t) \n Time To Recharge  4 weeks paid time off (accrued), 12 company holidays, and 3 weeks remote/flexible work per year \n Support to Parents  Up to 12 weeks of paid parental leave, regardless of your path to parenthood \n Learning \u0026 Development  $1,500 yearly towards your professional development e.g. conferences, courses, and other learning opportunities \n Team Connection  Team Lunches, quarterly off-sites, and regular town halls \n Financial Wellbeing  401K and/or Roth IRA, with 5% company contribution, even if you don’t! \n Flexible Spending Accounts  Pre-tax spend accounts for medical, dental/vision, dependent care, parking, and transit expenses \n Commute On Us  For those commuting up to 1 hour, put your rideshare cost on our company card and reclaim the drive-time to get work done! \n MatX E[x]tras  $50","salary_min":250000,"salary_max":600000,"location":"Mountain View, CA","workplace":"remote","job_type":"full-time","experience_level":"principal","tags":["pytorch","gpu","tensorflow","cloud"],"apply_url":"https://job-boards.greenhouse.io/matx/jobs/5225344008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T17:29:08Z","expires_at":"2026-06-29T14:13:29.128301Z","created_at":"2026-05-27T14:14:02.696883Z","updated_at":"2026-05-30T14:13:29.241055Z","company_name":"MatX","company_slug":"matx","company_logo_url":"https://www.google.com/s2/favicons?domain=matx.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/93fe1130-903b-482a-9d72-9c439f5cead5"},{"id":"9f4195d7-f223-4644-bba5-da9c9e66f839","company_id":"a0000000-0000-0000-0000-000000000001","title":"Product Manager, Developer Productivity","slug":"product-manager-developer-productivity-48e76021","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n As a Product Manager focused on Developer Productivity, you'll partner with Infrastructure, Inference, Research, and Product Engineering to build the systems that determine how thousands of engineers and researchers at Anthropic develop, build, test, and ship code—the foundation on which every model, evaluation, and product feature depends:\n \n Partner with Developer Productivity engineering teams to own the end-to-end developer experience—from the source control and language ecosystems that underpin our monorepo, to the build and CI infrastructure that keeps thousands of daily builds running reliably across multiple cloud providers, to the acceleration tooling that deeply integrates Claude into every engineer's workflow.\n Your work directly impacts engineering velocity across the entire company: defining the abstractions for how code moves from idea to production, establishing the metrics that surface friction before it compounds, and making the trade-offs that keep a rapidly scaling engineering organization shipping with confidence.\n You'll drive the evolution of our developer platform through a fundamental shift in how software gets built—as AI agents move from autocomplete to autonomous collaborators, the definition of \"developer\" is changing, and our tooling, governance, and workflows must change with it. You'll be defining what developer productivity means when a meaningful share of code is written, tested, and reviewed by Claude itself.\n You will define and own the strategy and roadmap across build systems, CI/CD pipelines, developer environments, accelerator toolchain management (GPU, TPU, Trainium), and the AI-native acceleration layer that makes Anthropic the most productive place in the world to build frontier AI.\n \n Responsibilities: \n \n Deeply understand the needs of internal customers across Research, Inference, Infrastructure, and Product—from researchers iterating on training code who need fast, reproducible builds to inference engineers managing compute-intensive toolchains with strict compatibility constraints.\n Define and iterate on the developer experience model: the workflows, tooling primitives, and feedback loops that govern how engineers and AI agents collaborate on code—including how we measure productivity when the unit of work is no longer a human typing.\n Partner with engineering leads to design build, CI, and test infrastructure that scales non-linearly with engineering headcount—ensuring that as Claude takes on more of the inner loop, the outer loop (review, validation, deployment) doesn't become the new bottleneck.\n Drive product strategy and roadmap for developer acceleration, including AI-assisted code review, agent-driven test generation, automated dependency management, and the governance frameworks that let teams safely delegate work to autonomous systems.\n Own the trade-off framework between velocity, reliability, security, and cost—making transparent prioritization decisions about where to invest in human workflows versus agent workflows, and communicating them clearly to senior leadership.\n Establish and champion the productivity metrics that matter in an AI-native engineering org—moving beyond commits and cycle time to measures that capture human-agent collaboration effectiveness, toil eliminated, and time-to-confident-ship.\n Build conviction about where developer tooling is headed on a 2–3 year horizon, and translate that into a roadmap that keeps Anthropic ahead of—not reacting to—the exponential curve of AI-assisted development.\n \n You may be a good fit if you have:\n \n 7+ years of product management experience, with deep exposure to developer tooling, build systems, CI/CD, or platform infrastructure\n Experience taking technical platform products from infancy to scale—you've built something from the ground up and grown it to serve demanding internal or external engineering customers\n Track record of building platform products that balance the needs of multiple engineering personas—you're comfortable making prioritization trade-offs between velocity, reliability, and security, and communicating them clearly\n Ability to internalize complex technical systems (build systems, monorepos, CI pipelines, accelerator toolchains) and translate that understanding into a comprehensive product vision\n Fluent across functions—you're equally credible discussing build graph optimization with engineers, developer velocity economics with leadership, and AI-agent governance with security teams\n A strong thesis on how AI will reshape software development—you've thought deeply about what changes when agent","salary_min":385000,"salary_max":595000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["agents","cloud","alignment","gpu","code-generation"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5220143008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T10:34:22Z","expires_at":"2026-06-29T14:00:19.557033Z","created_at":"2026-05-19T14:00:21.471137Z","updated_at":"2026-05-30T14:00:19.669103Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9f4195d7-f223-4644-bba5-da9c9e66f839"},{"id":"a61a8f6b-2794-46b5-8bcc-844df21181b0","company_id":"31ae48bc-c938-4c26-a348-0bf3c089a446","title":"Senior Software Engineer - Perf and Benchmarking","slug":"senior-software-engineer-perf-and-benchmarking-aee5d1a9","description":"CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com . \n About this role  \n We’re looking for a Senior Engineer for CoreWeave’s Benchmarking \u0026 Performance team. You will have an integral part in our planet-scale performance data warehouse: Ingesting, storing, transforming and analyzing performance events in all the data centers across our global infrastructure. You will also aid us in achieving industry-leading end-to-end performance benchmarking publications such as MLPerf.\n You will be an owner who leads designs, raises engineering standards, and delivers measurable improvements to latency, throughput, and reliability across multiple services. You’ll partner with product, orchestration, and hardware teams to evolve our Kubernetes-native platform and meet strict P99 SLAs at scale.\n What you’ll do \n \n Develop and enhance Kubernetes-native benchmarking services that measure latency, throughput, jitter, and cost-per-request across CoreWeave’s compute stack.\n Contribute to implementing and maintaining benchmarking workflows for end-to-end MLPerf Training and Inference runs, including workload setup, cluster configuration, and result validation.\n Participate in design discussions and contribute to architecture decisions within the team.\n Break down engineering tasks into clear milestones and deliver reliable, high-quality code.\n Collaborate with teammates to maintain reproducible, well-documented benchmarking processes.\n Provide constructive code reviews and share best practices with peers.\n Mentor junior engineers; review cross-team designs and elevate coding/testing standards.\n Help ensure reproducible, well-documented benchmarking processes. \n \n Who you are \n \n 3–5 years of experience building distributed systems, high-performance computing components, or cloud services.\n Strong programming skills in Python or Go (C++ a plus) with understanding of networked systems and performance fundamentals.\n Hands-on experience with Kubernetes in production environments plus familiarity with CI/CD and observability tools (e.g., Prometheus, Grafana, OpenTelemetry).\n Exposure to performance-critical GPU systems (CUDA, NCCL, NVLink/PCIe, memory bandwidth) or model-serving stacks (llm-d, vLLM, TensorRT-LLM, Megatron-LM).\n Effective communicator comfortable working cross-functionally. \n \n Nice to have \n \n Experience with time-series databases, LSM-based storage engines, or custom data pipelines.\n Familiarity with MLPerf or other large-scale benchmarking frameworks.\n Contributions to OSS projects such as llm-d, vLLM or PyTorch.\n Exposure to benchmarking GPU clusters or multi-region environments.\n Background working with CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies.\n \n The base salary range for this role is $182,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). \n What We Offer \n The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location.\n In addition to a competitive salary, we offer a variety of benefits to support your needs, including:\n \n Medical, dental, and vision insurance - 100% paid for by CoreWeave\n Company-paid Life Insurance \n Voluntary supplemental life insurance \n Short and long-term disability insurance \n Flexible Spending Account\n Health Savings Account\n Tuition Reimbursement \n Ability to Participate in Employee Stock Purchase Program (ESPP)\n Mental Wellness Benefits through Spring Health \n Family-Forming support provided by Carrot\n Paid Parental Leave \n Flexible, full-service childcare support with Kinside\n 401(k) with a generous employer match\n Flexible PTO\n Catered lunch each day in our office and data center locations\n A casual work environment\n A work culture focused on innovative disruption\n \n Our Workplace \n While we prioritize a hybrid work environment, remote work may be considered for candidates located more than 30 miles from an office, based on role requirements for specialized skill sets. New hires will be invited to attend onboarding at one of ","salary_min":182000,"salary_max":242000,"location":"Sunnyvale, CA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","gpu","distributed-systems","pytorch","llm"],"apply_url":"https://coreweave.com/careers/job?4681716006\u0026board=coreweave\u0026gh_jid=4681716006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-14T17:42:42Z","expires_at":"2026-06-29T14:04:52.999648Z","created_at":"2026-05-15T14:05:31.803205Z","updated_at":"2026-05-30T14:04:53.10912Z","company_name":"CoreWeave","company_slug":"coreweave","company_logo_url":"https://www.google.com/s2/favicons?domain=coreweave.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a61a8f6b-2794-46b5-8bcc-844df21181b0"},{"id":"3b319717-eb37-41e2-92a7-58d66a369cb0","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Robotics \u0026 Simulation Engineer, Discovery ","slug":"robotics-simulation-engineer-discovery-359cd14c","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n ABOUT THE TEAM\n The Discovery team at Anduril is at the forefront of incubating and maturing high-potential, software-defined, AI-native offerings that meet the toughest, newest challenges across hardware, software, space, and cyber domains. We're the architects of mission autonomy and mesh networking, delivering scalable hardware solutions that meet some of the most urgent national security needs. By working hand-in-hand with elite teams in Perception, AI, Motion Planning, Hardware, Test Engineering, Space, Networking, and Vehicle hardware, we craft cutting-edge, end-to-end systems that redefine mission success. \n ABOUT THE JOB\n We are seeking a Robotics \u0026 Simulation Engineer to own the simulation infrastructure, training pipelines, and deployment tooling that underpin our autonomous robotic systems. You will build and maintain the environments our RL and IL engineers train in, the telemetry systems that diagnose real-world failures, and the safety systems that protect operators and hardware during development and deployment. \n WHAT YOU’LL DO\n \n Own and scale simulation environments for RL training (Isaac Gym, Isaac Lab, MuJoCo), including terrain generation, articulated object models, and domain randomization configurations\n Build and maintain the training-to-deployment pipeline: model export, validation, on-robot inference, and rollback\n Develop telemetry and diagnostics tooling for real-time monitoring of robot state, policy performance, and anomaly detection during field testing\n Design and implement safety systems: software e-stops, joint limit monitors, fall detection interlocks, and operator alert systems\n Build Isaac Sim environments for tasks requiring articulated objects (vehicle models, doors, mechanical assemblies)\n Optimize training infrastructure for GPU cluster utilization and iteration speed\n Maintain robot URDF/MJCF models and ensure fidelity between sim and real\n \n REQUIRED QUALIFICATIONS\n \n 3+ years of experience in robotics simulation, robot software infrastructure, or ML training infrastructure\n Experience with physics simulation tools: NVIDIA Isaac Gym/Lab, MuJoCo, PhysX, or PyBullet\n Strong software engineering skills in Python; experience with C++ a plus\n Experience with URDF/MJCF model creation and maintenance\n Working knowledge of dynamics, controls, and robotic systems\n Experience building developer tooling, dashboards, or monitoring systems\n Eligible to obtain and maintain a U.S. security clearance\n \n PREFERRED QUALIFICATIONS\n \n Experience with NVIDIA Isaac Sim / Omniverse\n Experience with distributed training infrastructure or cluster management\n Prior work deploying ML models or control policies to real-world robotic systems\n Familiarity with ROS2, robot middleware, or real-time systems\n Prior work in defense technology\n US Salary Range\n $146,000 — $194,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:   \n  \n Benefits \n At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits . \n  \n \n Protecting Yourself from Recruitment Scams \n Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information.\n \n To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind:\n \n \n No Financial Requests:  Anduril will never solicit payment or demand ","salary_min":146000,"salary_max":194000,"location":"Costa Mesa, CA","workplace":"onsite","job_type":"full-time","experience_level":"mid","tags":["computer-vision","robotics","payments","gpu","distributed-systems","cloud"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5136834007?gh_jid=5136834007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-14T17:09:49Z","expires_at":"2026-06-29T14:06:46.773492Z","created_at":"2026-05-15T14:07:51.437243Z","updated_at":"2026-05-30T14:06:46.88825Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3b319717-eb37-41e2-92a7-58d66a369cb0"},{"id":"b9819fc1-996f-4825-bec6-a24dd9a53bdc","company_id":"0fc88a91-688e-421d-917d-4880569dd976","title":"Research Engineer, Voice","slug":"research-engineer-voice-ae96b0ee","description":"About Inflection AI \n Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We’re shaping the future of AI by combining emotional intelligence (EQ) and raw intelligence (IQ) to elevate people’s potential. Inflection AI created Pi, the world’s first emotionally intelligent AI, to help people work through decisions, emotions, and challenges. Pi is a personal AI agent powered by Inflection AI’s foundation model, proving that AI can be personal, empathetic, and contextually aware.\n About the Role \n We’re looking for a Member of Technical Staff (MTS), Research Engineer focused on voice and audio to help advance the spoken intelligence behind Pi. In this role, you’ll work at the intersection of research and production—developing, training, and shipping neural models across the full spectrum of voice: speech synthesis, recognition, audio generation, and real-time spoken dialogue. You’ll collaborate closely with ML engineers, product teams, and infrastructure to turn cutting-edge ideas in areas like neural audio codecs, diffusion-based TTS, and multimodal foundation models into the natural, expressive voice experiences that millions of Pi users interact with every day.\n What You’ll Do \n \n Research, develop, and optimize neural models for voice and audio—including text-to-speech, automatic speech recognition, audio generation, and spoken dialogue systems.\n Build and maintain production-grade training and inference pipelines for voice models, with close attention to latency, naturalness, and scalability.\n Run experiments end-to-end: data curation, model architecture design, training, evaluation, and ablation studies.\n Collaborate with ML engineers, product teams, and infrastructure to integrate voice models into Pi’s real-time conversational stack.\n Explore and apply advances in neural audio codecs, diffusion-based synthesis, streaming architectures, and multimodal foundation models to improve Pi’s voice experience.\n Develop robust evaluation frameworks combining perceptual metrics, automated benchmarks, and user-facing quality signals.\n Contribute to Inflection’s research culture through publications, internal reviews, and knowledge sharing.\n \n What We’re Looking For \n \n 2-5 years of research or engineering experience (including graduate work) in audio, speech, or multimodal ML.\n Strong proficiency in PyTorch and hands-on experience training and debugging large-scale neural models on GPU/accelerator clusters.\n Solid understanding of audio and speech fundamentals spectrograms, mel features, vocoders, codec-based representations, and signal processing.\n Demonstrated ability to take a research idea from prototype to production: equally comfortable reading papers and writing efficient, CUDA-aware training loops.\n Familiarity with modern generative architectures for audio (e.g., diffusion models, autoregressive codecs, flow-matching) and their trade-offs.\n Clear, collaborative communication able to distill complex research into actionable insights for cross-functional partners.\n Have a bachelor’s degree or equivalent in Computer Science, Electrical Engineering, Linguistics, or a related field; MS or PhD strongly preferred.\n \n Employee Pay Disclosures \n At Inflection AI, we aim to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contributions to the company. For this role, Inflection AI estimates a starting annual base salary to fall within the range of $225,000 to $325,000 , depending on a candidate’s qualifications and level of experience. This role also includes a meaningful equity component, allowing employees to share in the long-term success of the company.\n Benefits \n Inflection AI values and supports our team’s mental and physical health. We are focused on building a positive, safe, inclusive and inspiring place to work. Our benefits include: \n \n Diverse medical, dental and vision options \n 401k matching program \n Unlimited paid time off \n Parental leave and flexibility for all parents and caregivers\n Support of country-specific visa needs for international employees living in the Bay Area","salary_min":225000,"salary_max":325000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"junior","tags":["speech","pytorch","search","generative-ai","gpu","diffusion-models","agents","research"],"apply_url":"https://boards.greenhouse.io/inflectionai/jobs/4681124006?gh_jid=4681124006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-12T20:45:47Z","expires_at":"2026-06-29T14:04:38.202433Z","created_at":"2026-05-14T14:05:27.875309Z","updated_at":"2026-05-30T14:04:38.315108Z","company_name":"Inflection AI","company_slug":"inflection-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=inflection.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b9819fc1-996f-4825-bec6-a24dd9a53bdc"},{"id":"9dd96c9b-0c11-4da4-bd2a-3dc613470428","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Senior Product Manager, Inference","slug":"senior-product-manager-inference-d8adb377","description":"Who We Are \n Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.\n Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.\n We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.\n  \n What We're Looking For \n We're looking for a Founding Product Manager for Inference who will own this product end-to-end—roadmap, pricing, and GTM—from the ground up. This is a zero-to-one role at the intersection of deep technical fluency and commercial instinct. You'll define what we build and why, design the developer journey from first API call to production workload, and be the product voice in sales cycles and the market.\n The right candidate has lived inside the machine; they've operated model serving infrastructure or shipped products on top of it, and can move fluidly between a latency/throughput tradeoff conversation with an infra engineer and a positioning conversation with a sales lead. You will be joining the Product Team and report to our VP of Product working directly with our executive team as we grow this business. \n This is a hybrid role based in our New York City or San Francisco office with in-office requirements of 2 days per week. \n What You’ll Do \n \n Define Lightning AI's inference product vision and roadmap — what we build, what we don't, and in what order — translating the competitive landscape (vLLM, Together, Fireworks, Modal, hyperscaler inference APIs) into a differentiated strategy grounded in Lightning's compute and software advantage\n Own inference pricing and packaging end-to-end: design the model (per-token, per-second, reserved capacity), run pricing experiments with Growth and Finance, and define the tiers that convert self-serve developers into enterprise contracts\n Be the product voice in GTM: develop sales positioning, answer technical objections in the field, and partner with Marketing on the benchmarks, reference architectures, and developer content that builds credibility with ML engineers and platform teams\n Own the developer journey from API key to production-scale deployment — identify and remove friction across onboarding, documentation, SDK ergonomics, and dashboard observability\n Lead experiments across activation flows, pricing pages, and upgrade prompts; track and move DAU/MAU, Time to Value, Activation %, PQLs, and expansion revenue\n Partner with engineering to write tight specs and make fast build/buy/partner decisions; collaborate across Product to ensure inference coheres with training, fine-tuning, and storage surfaces\n Establish inference-specific metrics — throughput, latency SLAs, cold-start behavior, cost per token — and build the instrumentation to track them\n \n What You’ll Need \n \n 7+ years of product management experience, with at least 3 years in infrastructure, platform, or developer tooling products\n Direct, hands-on experience with model serving or inference infrastructure — you've shipped in this space; you understand quantization, batching strategies, KV cache, and speculative decoding at a level that lets you go deep with ML engineers\n Proven track record owning product pricing and packaging decisions, not just feature decisions — you've modeled unit economics and made calls that affected margin\n Experience with a PLG or trial-to-paid motion in a developer product; you know how to build self-serve growth loops and run rigorous A/B experiments\n Strong analytical skills — comfortable with product instrumentation, metrics, and dashboards; you pull your own data\n Excellent written and verbal communication; you can write a crisp one-pager, a technical spec, and a customer-facing benchmark brief with equal fluency\n Bias for action and comfort operating with high ambiguity in a fast-moving environment\n Bachelor's degree in Computer Science, Engineering, or related technical field (or equivalent practical experience)\n Bonus: Prior experience at a neocloud, hyperscaler inference team, or AI infrastructure startup; familiarity with the PyTorch/Lightning ecosystem; background in GPU cluster products or consumption-based infrastructure pricing\n \n  \n Compensation \n We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles incl","salary_min":160000,"salary_max":275000,"location":"New York, NY","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["pytorch","mlops","fine-tuning","gpu","llm","inference"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/7702094003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-12T18:21:09Z","expires_at":"2026-06-29T14:03:03.748651Z","created_at":"2026-05-14T14:03:38.883824Z","updated_at":"2026-05-30T14:03:03.857752Z","company_name":"Lightning AI","company_slug":"lightning-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=lightning.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9dd96c9b-0c11-4da4-bd2a-3dc613470428"},{"id":"eeec3dfe-e958-49cf-b0f5-e7a18df0ea3f","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Machine Learning Systems Engineer","slug":"machine-learning-systems-engineer-f009afd8","description":"Mission Summary: We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models. \n What you'll be doing: \n \n Performance Profiling \u0026 Optimization : Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time.\n Distributed Training : Optimize distributed training pipelines using frameworks such as PyTorch Distributed.\n Kernel Development : Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.\n Data Pipeline Engineering : Optimize robust data loading pipelines that maximize training throughput. \n \n What we're looking for :\n \n Education : Bachelor’s, Master’s degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline.\n Software Engineering : Strong proficiency in Python.\n ML Frameworks : Extensive hands-on experience with PyTorch.\n ML Knowledge : Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes.\n Problem Solving : Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges. \n \n We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  \n Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. \n Salary Range\n $144,000 — $192,000 USD \n Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. \n Our journey is always people first. \n We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.\n Higher purpose, greater impact. \n We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.  \n Scale up, not starting up. \n Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.\n Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit  www.Motional.com and follow us on Twitter , LinkedIn ,  Instagram and YouTube .\n Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibilit","salary_min":144000,"salary_max":192000,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"mid","tags":["autonomous-vehicles","distributed-systems","data-pipeline","gpu","pytorch","machine-learning","infrastructure"],"apply_url":"https://motional.com/open-positions/?gh_jid=7730611003#/7730611003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-11T16:11:39Z","expires_at":"2026-06-29T14:05:58.264231Z","created_at":"2026-05-12T14:07:02.423397Z","updated_at":"2026-05-30T14:05:58.381259Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/eeec3dfe-e958-49cf-b0f5-e7a18df0ea3f"},{"id":"ffe066a0-b346-42e2-9192-3ada3ff04251","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Machine Learning Systems Engineer","slug":"machine-learning-systems-engineer-12ede1b6","description":"Mission Summary: We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models. \n What you'll be doing: \n \n Performance Profiling \u0026 Optimization : Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time.\n Distributed Training : Optimize distributed training pipelines using frameworks such as PyTorch Distributed.\n Kernel Development : Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.\n Data Pipeline Engineering : Optimize robust data loading pipelines that maximize training throughput. \n \n What we're looking for :\n \n Education : Bachelor’s, Master’s degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline.\n Software Engineering : Strong proficiency in Python.\n ML Frameworks : Extensive hands-on experience with PyTorch.\n ML Knowledge : Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes.\n Problem Solving : Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges. \n \n We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  \n Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. \n Salary Range\n $144,000 — $192,000 USD \n Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. \n Our journey is always people first. \n We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.\n Higher purpose, greater impact. \n We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.  \n Scale up, not starting up. \n Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.\n Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit  www.Motional.com and follow us on Twitter , LinkedIn ,  Instagram and YouTube .\n Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibilit","salary_min":144000,"salary_max":192000,"location":"Las Vegas, Nevada, United States","workplace":"hybrid","job_type":"full-time","experience_level":"mid","tags":["pytorch","gpu","autonomous-vehicles","data-pipeline","distributed-systems","infrastructure","machine-learning"],"apply_url":"https://motional.com/open-positions/?gh_jid=7730610003#/7730610003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-11T16:11:38Z","expires_at":"2026-06-29T14:05:58.105374Z","created_at":"2026-05-12T14:07:02.520485Z","updated_at":"2026-05-30T14:05:58.215087Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ffe066a0-b346-42e2-9192-3ada3ff04251"},{"id":"c1f0a92b-ce08-4044-9b3e-29321167293c","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Machine Learning Systems Engineer","slug":"machine-learning-systems-engineer-6720a55f","description":"Mission Summary: We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models. \n What you'll be doing: \n \n Performance Profiling \u0026 Optimization : Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time.\n Distributed Training : Optimize distributed training pipelines using frameworks such as PyTorch Distributed.\n Kernel Development : Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.\n Data Pipeline Engineering : Optimize robust data loading pipelines that maximize training throughput. \n \n What we're looking for :\n \n Education : Bachelor’s, Master’s degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline.\n Software Engineering : Strong proficiency in Python.\n ML Frameworks : Extensive hands-on experience with PyTorch.\n ML Knowledge : Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes.\n Problem Solving : Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges. \n \n We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  \n Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. \n Salary Range\n $144,000 — $192,000 USD \n Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. \n Our journey is always people first. \n We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.\n Higher purpose, greater impact. \n We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.  \n Scale up, not starting up. \n Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.\n Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. Headquartered in Boston, Motional has operations in the U.S and Asia. For more information, visit  www.Motional.com and follow us on Twitter , LinkedIn ,  Instagram and YouTube .\n Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E-Verify. All newly-hired employees are queried through this electronic system established by the DHS and the SSA to verify their identity and employment eligibilit","salary_min":144000,"salary_max":192000,"location":"Pittsburgh, PA","workplace":"hybrid","job_type":"full-time","experience_level":"mid","tags":["distributed-systems","gpu","autonomous-vehicles","data-pipeline","pytorch","infrastructure","machine-learning"],"apply_url":"https://motional.com/open-positions/?gh_jid=7730609003#/7730609003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-11T16:11:37Z","expires_at":"2026-06-29T14:05:58.348728Z","created_at":"2026-05-12T14:07:02.265793Z","updated_at":"2026-05-30T14:05:58.460864Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c1f0a92b-ce08-4044-9b3e-29321167293c"}],"page":1,"per_page":20,"total":533,"total_pages":27}