{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n About the Team\n The Counter Intrusion MSE team develops systems that provide force protection capabilities, monitoring the perimeter of secure areas, land or sea, for approaching people, vehicles, and vessels. We live in a world where security officers are increasingly overwhelmed by sensor data feeds. Our products leverage advanced sensor fusion and autonomy to seamlessly render activity in the environment to Lattice's common operating picture. This relieves operators of our system from a ton of burden and allows our customers to get more done with fewer personnel. Increasingly, Counter Intrusion MLEs are working on projects to bring customers' existing legacy security systems into the Lattice ecosystem. Key to this effort is building scalable software solutions so that we can service many customers without requiring bespoke software for each one. The Counter Intrusion team is responsible for the development, testing, deployment, and sustainment of our family of systems. We work closely with other teams from product, engineering, sales, logistics, operations, and mission success.\n About the Role\n We are looking for a Machine Learning Engineer to apply the latest research to solve our toughest problems. This role will be responsible for owning the whole machine learning stack for the Counter Intrusion team. You will design and train multi-sensor object detection models for perception on edge compute devices that push the boundaries of what's possible with our sensors. You will develop learning algorithms to optimize the behavior of autonomous systems. You will also be responsible for the end-to-end design, implementation, and performance of the ML stack, including the infrastructure for data collection and training. Your work will directly impact Anduril's ability to deliver cutting-edge defense technology, with the opportunity to identify and develop novel ML applications across our product portfolio.\n Responsibilities\n \n Propose and prototype innovative solutions to solve real-world problems, leveraging the latest state-of-the-art techniques in the field\n Develop and maintain core ML pipelines\n Train and deploy deep learning models for real-time applications\n Collaborate cross-functionally with camera, systems and labeling teams\n Curate datasets for evaluating performance and comparing performance trends over time\n Provide technical mentorship to other junior ML engineers\n \n Required Qualifications\n \n MS or PhD in Machine Learning, Robotics or Computer Science, with emphasis on Computer Vision\n BS in Computer Science, Machine Learning, Electrical Engineering, or related field\n 6+ years of experience developing, benchmarking and optimizing ML algorithms on large-scale datasets\n Strong Deep Learning and CV background\n Proficiency in C++ development in a Linux environment\n Experience with Python development and deep learning frameworks such as PyTorch, JAX and TensorFlow\n Experience deploying models with TensorRT and ONNX\n Optimize on-device inference and vision kernels across CPU/GPU/NPU\n Track record of developing and deploying CV models from R\u0026D to production\n Experience writing and maintaining automated continuous integration tests\n Knowledge of system profiling and tuning for latency, memory and power efficiency\n Ability to conduct experiments, ablation studies and create highly detailed reports\n Eligible to obtain and maintain a U.S. Secret security clearance\n \n Preferred Qualifications\n \n Experience in one or more of the following:\n \n Object Detection, Object Tracking, Instance Segmentation, Semantic Segmentation, Semantic Change Detection, Depth Estimation, Model Pruning and Compression\n \n Experience in one or more of the following:\n \n Visual Odometry, SLAM, Multi-view Geometry, Camera Models, RGB-D and LIDAR Sensor Fusion, Optical Flow\n \n Experience troubleshooting and analyzing remotely deployed software systems\n 1 year of experience in a technical leadership role\n US Hourly Range \n $220,000 — $292,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Hi","salary_min":220000,"salary_max":292000,"location":"Irvine, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["jax","computer-vision","deep-learning","tensorflow","robotics","pytorch","cloud","payments"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5256999007?gh_jid=5256999007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-05T17:08:54Z","expires_at":"2026-11-13T16:57:16.198459Z","created_at":"2026-10-14T15:21:01.365645Z","updated_at":"2026-10-14T16:57:16.201756Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6c602d86-d419-4ce2-929d-1dbc939432ed"},{"id":"5e7d165a-b53c-4ed3-9498-73273b6b1e83","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer / Research Scientist, RL Frontiers","slug":"research-engineer-research-scientist-rl-frontiers-f4d3a293","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 Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale.\n This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet.\n Key responsibilities\n \n Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient\n Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale\n Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic\n Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale\n Own end-to-end performance of our largest RL runs, from research code down to the hardware\n Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled\n Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause\n \n Minimum qualifications\n \n Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization\n Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism\n A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact\n Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute\n Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm\n Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack\n \n Preferred qualifications\n \n Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise\n Experience developing RL algorithms for language models\n Experience with scaling laws or other quantitative models of training efficiency\n Experience designing or modifying transformer architectures beyond standard configurations\n Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale\n Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability\n Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices\n Experience with C++ or Rust\n \n Representative projects\n \n Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks\n Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline\n Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch\n Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system\n Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel\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 $500,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["alignment","jax","reinforcement-learning","cloud","pytorch","search","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5438044008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T19:03:37Z","expires_at":"2026-11-13T16:54:56.207892Z","created_at":"2026-10-14T15:17:22.661233Z","updated_at":"2026-10-14T16:54:56.210539Z","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/5e7d165a-b53c-4ed3-9498-73273b6b1e83"},{"id":"e0de87af-ad73-43f6-a502-ac3d19f5bc91","company_id":"4c0fefc3-173a-4227-a823-4d67d3e70ff0","title":"Research Engineer, Climate Modeling ","slug":"research-engineer-climate-modeling-5afb0300","description":"Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.\n Our base salary range is $146,880 - $220,320 and in addition we have generous bonus plans to provide a competitive compensation package.  \n We will begin reviewing applications on October 19, 2026. \n Who You Are: \n We seek a Research Engineer for a new three-year project to develop an AI-powered physics-informed hybrid climate model trained on historical data that is capable of multidecadal forecasts that are more accurate than the current state of the art. We’re looking for someone passionate about improving the quality, efficiency and accessibility of climate models with AI and modern software engineering.  \n This position is supported by a grant with a current period of performance ending September 22, 2029. Continuation of the position beyond that date depends on renewal, extension, or identification of additional funding. Ai2 will make reasonable efforts to notify the employee if funding for the position is expected to end. \n Who We Are:  \n Ai2’s Climate Modeling team is an international pioneer in using machine learning (ML) methodologies to improve on current physics-based climate models.  Our fast, accurate autoregressive open-source emulator, ACE, stably reproduces the climate and weather extremes of an existing climate model or a reanalysis. We have coupled ACE to a ML ocean model, trained it to  realistically account  for changing CO2 concentrations, and downscaled (super-resolved) ACE outputs from their 100 km native scale to to km-scale detail. Our tight-knit team of scientists and software engineers works closely with leading physics-based climate modeling centers to obtain unique training and testing data and get expert feedback on our progress. We collaborate with other leading research groups doing related AI work.\n Your Next Challenge: \n Physically-based climate models discretize equations representing individual time-evolving processes like clouds, rain, wind, land and sea-ice, and ocean currents on a computational grid.  Some processes (e.g. clouds) are less reliably encoded than others (e.g. winds).  Hence such climate models have biases in representing present-day climate and produce an undesirably large range of projections of future climate change for a given human-caused change in CO2 or other climate forcings.  Current AI-based climate models reduce present-day climate bias, but don’t generalize well to future climate change.\n We are starting a 3-year project funded by google.org  to develop an open source ‘AI-first’ climate model that can demonstrably project future climates more accurately than current physically-based climate models when trained on historical observations by judicious design of the AI to incorporate physical principles that reliably apply in any climate, seen or unseen.   The model should be lightweight and easy for an ML-savvy climate scientist to train and deploy. Our starting point is the Google Research NeuralGCM model, a hybrid ML model that uses a conventional discretization of winds encoded in JAX and learns a column-local representation of all other atmospheric and land processes from historical data. Compared to other AI climate models, NeuralGCM uses ML in a conceptually simpler way and shows somewhat better future-climate generalization skill.   It is an open-weight model but its training pipeline  and documentation need to be adapted for a broader user community.\n You will join a small team to develop the desired NeuralGCM+ model and foster its uptake by the climate modeling community.  This includes coupling it to an ML-based ocean model and designing approaches to demonstrably improve its skill in unseen climates.  This position has a three year term; there is potential for longer-term funding if  the NeuralGCM+ project demonstrates exceptional progress.\n You'll get to work on:\n \n Adapting the training pipeline and data needs of NeuralGCM to run efficiently on Google Cloud resources and on NVIDIA GPUs.  Note that NeuralGCM is written in JAX and implemented on TPUs.\n Documenting this training pipeline and distributing code on a GitHub repo.\n Implementing and testing a strategy for coupling NeuralGCM with the chosen ML ocean model.  \n Open-source code development as part of a tightly connected team, including daily meetings, code review, design documents and external collaborators. \n Develop and present tutorials on NeuralGCM+ to interested user groups.\n \n What You’ll Need: \n \n 2-4 years of experience building and deploying a relevant ML application in a practical or academic research setting and be fluent in Python and collaborative coding practices.\n A B.Sc. in computer or computational science, atmospheric science or a related geophysical science, or applied mathematics/statistics.\n Experience i","salary_min":146880,"salary_max":220320,"location":"Seattle, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["search","robotics","jax","research"],"apply_url":"https://job-boards.greenhouse.io/thealleninstitute/jobs/8234447","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T22:53:55Z","expires_at":"2026-11-13T17:02:42.380835Z","created_at":"2026-10-14T15:24:45.312892Z","updated_at":"2026-10-14T17:02:42.383908Z","company_name":"Allen Institute for AI","company_slug":"allen-institute-for-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=allenai.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e0de87af-ad73-43f6-a502-ac3d19f5bc91"},{"id":"7072c343-0574-44ce-8749-2b20e4c5d8f5","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"ML Infrastructure Engineer","slug":"ml-infrastructure-engineer-74e429b8","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\n ABOUT THE ROLE: \n As an ML Infrastructure Engineer, you will play a pivotal role in building and optimizing the reliable, high-performance ML platform that powers recommendations on X. We're looking for exceptional engineers who are passionate about our mission and have a strong desire to make a meaningful impact.\n RESPONSIBILITIES: \n \n Designing, building, and scaling GPU compute infrastructure, training frameworks, and experimentation tools to enable rapid iteration on ML hypotheses\n Developing data pipelines and integrating large-scale data, training, and inference systems\n Collaborating with ML teams to productionize models and ensure seamless integration across the stack\n Ensuring scalability, reliability, and efficiency of large-scale machine learning systems\n Working across the full stack to solve complex problems independently\n Mentoring junior engineers and contributing to the growth of the team\n \n BASIC QUALIFICATIONS: \n \n Bachelor, Master, Post-graduate or PhD in computer science, machine learning, or other quantitative discipline; or equivalent work experience\n 2+ years of industry experience working with high traffic or large-scale production environments, distributed systems, GPU infrastructure, and/or deep learning applications\n 2+ years experience with ML platforms, training infrastructure, or close collaboration with modeling engineers and data scientists\n Strong proficiency with Python and experience with compiled languages such as C++ or Rust\n \n PREFERRED SKILLS AND EXPERIENCE: \n \n Deep familiarity with modern ML frameworks such as JAX or PyTorch\n Low-level understanding of compute systems, including distributed storage, NVIDIA drivers, CUDA toolkits, and networking\n Comfortable with Linux systems and orchestration tools\n Experience with job schedulers (e.g., Slurm), configuration management (Puppet/Ansible), or related infrastructure tooling\n \n COMPENSATION AND BENEFITS: \n $180,000 - $440,000 USD \n Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks. \n SpaceXSI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["gpu","jax","deep-learning","distributed-systems","data-pipeline","pytorch","infrastructure","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5193037007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-22T03:49:23Z","expires_at":"2026-11-13T16:55:31.729941Z","created_at":"2026-07-22T14:03:40.513428Z","updated_at":"2026-10-14T16:55:31.73278Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7072c343-0574-44ce-8749-2b20e4c5d8f5"},{"id":"021f3b70-f0d5-4666-a5e1-431d120b0e63","company_id":"31ae48bc-c938-4c26-a348-0bf3c089a446","title":"Senior Software Engineer - GPU Kernel Authoring \u0026 Optimization","slug":"senior-software-engineer-gpu-kernel-authoring-optimization-d4eed12b","description":"CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com . \n About the role: \n CoreWeave is the top-rated AI-cloud for high-performance GPU infrastructure across AI/ML, visual effects, rendering, and real-time inference. Our stack is engineered for speed, scale, and cost-efficiency—an unmatched alternative to traditional hyperscalers. At CoreWeave, infrastructure is the product.\n We're looking for a Senior Engineer for CoreWeave's Benchmarking \u0026 Performance team, focused on kernel authoring and optimization. You will write, profile, and tune the GPU kernels that sit on the critical path of large-scale model serving—squeezing maximum throughput and minimum latency out of every SM, tensor core, and byte of memory bandwidth. 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 our inference stack. You'll partner with product, orchestration, and hardware teams to turn kernel-level wins into end-to-end gains and meet strict P99 SLAs at scale.\n \n Author, profile, and optimize CUDA kernels—GEMMs, attention, MoE routing, quantization, KV-cache, and fused epilogues—on the critical path of LLM inference.\n Optimize for the hardware: exploit tensor cores and tune occupancy, memory coalescing, shared-memory/register usage, and overlap of compute with data movement.\n Use kernel-authoring DSLs and compilers to prototype and ship kernels quickly without sacrificing performance.\n Benchmark rigorously: build reproducible microbenchmarks and roofline analyses, and validate that kernel-level wins translate to end-to-end latency/throughput gains across model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang).\n Implement and maintain benchmarking workflows for end-to-end MLPerf Inference (and Training) runs, including workload setup, cluster configuration, runbooks, and result validation.\n Lead design reviews and drive architecture within the team; decompose multi-service work into clear milestones.\n Mentor junior engineers; review cross-team designs and elevate coding/testing standards.\n Help ensure reproducible, well-documented benchmarking and kernel-optimization processes.\n \n Who You Are: \n \n 5+ years of experience building high-performance computing, GPU/accelerator software, or performance-critical systems.\n Hands-on CUDA experience is required—you have written and optimized custom kernels and are fluent with the CUDA programming and memory model.\n Deep understanding of GPU architecture and performance: tensor cores, warp/occupancy tuning, the memory hierarchy and bandwidth, NVLink/PCIe, and profiling with Nsight Compute/Systems.\n Strong coding in C++ and Python; comfortable reading and writing low-level, performance-sensitive code.\n Familiarity with model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang) and the kernels that dominate their inference cost.\n Strong communicator comfortable collaborating with cross-functional teams and external partners.\n \n Preferred: \n \n Triton or Mojo for authoring custom GPU kernels — highly desired.\n CuTe DSL for Python-based kernel authoring on NVIDIA GPUs.\n JAX and its Pallas kernel language for authoring kernels on GPU/TPU.\n HIP / ROCm and AMD GPU experience.\n NCCL and collective-communication performance.\n Experience with alternative accelerators such as Google TPUs and Meta's MTIA.\n Familiarity with kernel-authoring DSLs and nano-compilers such as KNYFE and its Block DSL.\n Experience with Kubernetes at production scale.\n Experience with SUNK (Slurm on Kubernetes) / Slurm for scheduling large GPU jobs.\n Experience running MLPerf submissions or similar large-scale audited benchmarks.\n Contributions to OSS projects such as vLLM, SGLang, PyTorch, Triton, or CUTLASS.\n \n Wondering if you're a good fit? \n We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match.\n Why CoreWeave? \n Help shape an industry-defining inference platform that enables teams to deploy generative AI and real-time applications at scale. If squeezing every last microsecond out of GPU kernels and delivering reliable model serving excites you, this is the place to build. We're in an exciting stage of hyper-growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly ","salary_min":182000,"salary_max":242000,"location":"Sunnyvale, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","jax","mlops","gpu","generative-ai","computer-graphics","llm"],"apply_url":"https://coreweave.com/careers/job?4697100006\u0026board=coreweave\u0026gh_jid=4697100006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T22:01:55Z","expires_at":"2026-11-13T16:56:02.349742Z","created_at":"2026-07-15T14:06:51.909822Z","updated_at":"2026-10-14T16:56:02.352908Z","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/021f3b70-f0d5-4666-a5e1-431d120b0e63"},{"id":"b0055a15-dc7b-4101-a70c-ea36d54ec262","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Machine Learning Engineer, Maneuvering Tech","slug":"machine-learning-engineer-maneuvering-tech-349ae6ba","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Driver Refinement and Validation’s (DRV) mission is to build upon the high-quality ML Driver developed by Driver Intelligence, refining its performance to improve autonomous driving in specific, complex real-world contexts and problem areas (e.g., Ride-Hailing contexts). It aims to ensure that the Waymo Driver always computes safe, compliant, comfortable, and useful plans. As an Engineer in DRV, you will bridge the gap between cutting-edge machine learning and real-world vehicle performance, engineering the validation and onboard systems that ensure the autonomous vehicle reliably executes intelligent and predictable maneuvers.\n  \n You will: \n \n Develop next-generation, ML-powered systems that enhance the capabilities of the ML driver and accelerate the rapid scaling of Waymo’s business\n Fine-tune, adapt, and benchmark state-of-the-art generative models to improve autonomous vehicle perception, motion prediction, and trajectory planning\n Write high-quality, scalable, and thoroughly tested code to bring cutting-edge ML into production\n Partner with world-class engineers and product managers to deliver safe and smooth autonomous vehicle behaviors\n \n You have: \n \n MS in Computer Science, Machine Learning, Robotics, or related technical field, or equivalent practical experience\n 3+ years of industry experience deploying, evaluating, and maintaining ML-based systems in real-world, production environments\n Proficient programming skills in Python and/or C++, coupled with strong analytical and debugging abilities\n Expertise with modern deep learning frameworks, such as  JAX or PyTorch\n \n We prefer: \n \n Experience in deep learning, reinforcement learning, and ML systems\n Domain expertise in solving prediction, behaviors, motion planning, or related robotics problems\n ML post-training experience\n Prior industry experience (e.g. internships) in applied ML or software development\n Publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA,  IROS, RSS, CoRL, ACL, or EMNLP)\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["autonomous-vehicles","deep-learning","jax","robotics","pytorch","nlp","reinforcement-learning","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8039191","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-30T18:18:55Z","expires_at":"2026-11-13T16:55:54.943251Z","created_at":"2026-10-14T15:20:04.389786Z","updated_at":"2026-10-14T16:55:54.946264Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b0055a15-dc7b-4101-a70c-ea36d54ec262"},{"id":"cd19852e-c027-49b0-9783-0524cfb3e1ce","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff Machine Learning Engineer, Maneuvering Tech","slug":"staff-machine-learning-engineer-maneuvering-tech-81c47f64","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Driver Refinement and Validation’s (DRV) mission is to build upon the high-quality ML Driver developed by Driver Intelligence, refining its performance to improve autonomous driving in specific, complex real-world contexts and problem areas (e.g., Ride-Hailing contexts). It aims to ensure that the Waymo Driver always computes safe, compliant, comfortable, and useful plans. As an Engineer in DRV, you will bridge the gap between cutting-edge machine learning and real-world vehicle performance, engineering the validation and onboard systems that ensure the autonomous vehicle reliably executes intelligent and predictable maneuvers.\n  \n You will: \n \n Develop next-generation, ML-powered systems that enhance the capabilities of the ML driver and accelerate the rapid scaling of Waymo’s business\n Fine-tune, adapt, and benchmark state-of-the-art generative models to improve autonomous vehicle perception, motion prediction, and trajectory planning\n Write high-quality, scalable, and thoroughly tested code to bring cutting-edge ML into production\n Partner with world-class engineers and product managers to deliver safe and smooth autonomous vehicle behaviors\n \n You have: \n \n MS in Computer Science, Machine Learning, Robotics, or related technical field, or equivalent practical experience\n 8+ years of industry experience deploying, evaluating, and maintaining ML-based systems in real-world, production environments\n Proficient programming skills in Python and/or C++, coupled with strong analytical and debugging abilities\n Expertise with modern deep learning frameworks, such as  JAX or PyTorch\n \n We prefer: \n \n Experience in deep learning, reinforcement learning, and ML systems\n Domain expertise in solving prediction, behaviors, motion planning, or related robotics problems\n ML post-training experience\n Prior industry experience (e.g. internships) in applied ML or software development\n Publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA,  IROS, RSS, CoRL, ACL, or EMNLP)\n The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.\n Hourly PhD Pay\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["jax","nlp","autonomous-vehicles","robotics","reinforcement-learning","pytorch","deep-learning","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8001059","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-11T16:37:09Z","expires_at":"2026-11-13T16:55:55.194067Z","created_at":"2026-10-14T15:20:04.647131Z","updated_at":"2026-10-14T16:55:55.197195Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cd19852e-c027-49b0-9783-0524cfb3e1ce"},{"id":"0dcc2e8e-a8cd-449a-9eae-c3c6916b5b85","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Multimodal Understanding","slug":"member-of-technical-staff-multimodal-understanding-d5a4390f","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\n ABOUT THE ROLE: \n You will join the multimodal team to push toward superhuman multimodal intelligence. Advance understanding and generation across modalities—image, video, audio, and text—spanning the full stack: data curation/acquisition, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences.\n Collaborate cross-functionally with pre-training, post-training, reasoning, data, applied, and product teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration. Contribute to building models that can see, hear, reason about, and interact with the world in real time at unprecedented levels.\n RESPONSIBILITIES: \n \n Design, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale.\n Develop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text).\n Advance multimodal capabilities including spatial-temporal compression, cross-modal alignment, world modeling, reasoning, emergent abilities, audio/image/video understanding \u0026 generation, real-time video processing, and noisy data handling.\n Drive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models.\n Create evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy.\n Innovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance.\n Build research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback.\n Work across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions.\n \n BASIC QUALIFICATIONS: \n \n Hands-on experience with multimodal pre-training, post-training, or fine-tuning (vision, audio, video, or cross-modal).\n Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA.\n Proven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design).\n Deep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data.\n Strong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors).\n Proactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers.\n Willingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences.\n \n PREFERRED SKILLS AND EXPERIENCE:\n \n Experience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling.\n Familiarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems.\n Proficiency in Rust and/or C++ for performance-critical components.\n Hands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes.\n Background building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership.\n Passion for end-to-end user experience in interactive, real-time multimodal AI systems.\n \n COMPENSATION AND BENEFITS: \n $180,000 - $440,000 USD\n Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n SpaceXSI is an equal opportunity empl","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","llm","reinforcement-learning","pytorch","agents","generative-ai","jax","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5111374007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-17T18:05:41Z","expires_at":"2026-11-13T16:55:31.702941Z","created_at":"2026-04-17T19:30:23.022034Z","updated_at":"2026-10-14T16:55:31.705874Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0dcc2e8e-a8cd-449a-9eae-c3c6916b5b85"},{"id":"77a0971d-1fd6-40d3-bac3-b090393add8d","company_id":"e452e377-b504-47ba-85cc-b47aa09c3067","title":"AI/ML Research Engineer","slug":"aiml-research-engineer-91328b77","description":"Manifold Bio is a platform biotechnology company pioneering AI-guided protein design and massively multiplexed in vivo screening to unlock tissue-targeted medicines and organism-scale models of living systems. Using proprietary molecular barcoding technology, we screen hundreds of thousands of protein designs simultaneously in living systems, producing in vivo-validated datasets at a scale no one else can match. The datasets power our computational models, which leads to better drug designs, creating a flywheel that gets stronger with every campaign. Our team of protein engineers, biologists, and computational scientists works  across this full stack to pursue programs both internally and with leading pharma companies. \n  \n Position \n Manifold Bio is seeking a talented Machine Learning Research Engineer to join our growing AI team. You will work closely with our research scientists to implement, scale, and optimize machine learning systems that power our de novo antibody design platform and advance our protein design capabilities. Your efforts will contribute to building production-ready ML infrastructure that enables breakthrough discoveries in protein therapeutics. You will be expected to take ownership of engineering challenges in our ML pipeline, from data processing and model training to deployment and monitoring, while collaborating closely with our research team to translate cutting-edge ideas into robust, scalable systems.\n This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate.  \n Responsibilities \n \n Implement and optimize machine learning models for protein design\n Build and maintain scalable data processing pipelines for large-scale protein and molecular datasets\n Develop and deploy ML infrastructure for distributed training and inference across GPU clusters\n Collaborate with research scientists to translate experimental ML approaches into production-ready code\n Design and execute ML experiments with clear hypotheses and rigorous analysis\n Optimize model performance and computational efficiency for large-scale protein design tasks\n Build tools and utilities to support rapid prototyping and experimentation by the research team\n \n Required Qualifications \n \n Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field\n 2+ years of hands-on experience with PyTorch and/or JAX for deep learning applications\n Strong proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)\n Experience with distributed computing and GPU optimization techniques\n Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences\n Understanding of modern deep learning architectures and optimization techniques\n Experience implementing research papers or translating ML approaches to production systems\n Proficiency with version control (Git), testing frameworks, and software engineering best practices\n Strong problem-solving skills and ability to work independently on technical challenges\n Excellent written and verbal communication skills for cross-functional collaboration\n \n Preferred Qualifications \n \n Experience training LLMs or diffusion generative models\n Knowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes)\n Background in computational biology, bioinformatics, or structural biology\n Experience with large-scale data engineering and ETL pipelines\n Familiarity with MLOps practices and model deployment frameworks\n \n This Role Might Be Perfect For You If \n You are passionate about leveraging state of the art machine learning approaches to solve challenging disease areas \n \n You enjoy translating research ideas into high impact, productionized, scalable code\n You have rich AI/ML experience and are looking to pivot into biotech\n \n If you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to careers@manifold.bio . \n  \n Base Salary Range: $140,000-225,000\n This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.\n This role is eligible for:\n \n Annual performance-based target bonus\n Stock options\n Comprehensive medical, dental, and vision coverage\n 401(k) plan\n Flexible paid time off and holidays\n Perks including on-site gym, onsite lunch, and commuter support\n \n Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.\n We value different experiences and ways of thinking and believe the most talented teams are built by bringing together people of diverse cultures, genders, and backgrounds.","salary_min":140000,"salary_max":225000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["data-pipeline","deep-learning","mlops","gpu","llm","pytorch","distributed-systems","jax"],"apply_url":"https://job-boards.greenhouse.io/manifoldbio/jobs/5106191007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-13T15:11:37Z","expires_at":"2026-11-13T17:00:00.106936Z","created_at":"2026-04-16T18:53:13.262577Z","updated_at":"2026-10-14T17:00:00.111856Z","company_name":"Manifold Bio","company_slug":"manifold-bio","company_logo_url":"https://www.google.com/s2/favicons?domain=manifoldbio.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/77a0971d-1fd6-40d3-bac3-b090393add8d"},{"id":"41e20e83-f1c0-412c-b84c-10a516f9fc81","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior/Staff Deep Reinforcement Learning Engineer - DoorDash Dot","slug":"seniorstaff-deep-reinforcement-learning-engineer-ad0ce130","description":"About the Team\n Our DD Labs team builds real-time autonomous delivery systems. The Planning \u0026 Decision-Making group is investing heavily in deep reinforcement learning to move beyond classical planning, learning policies that generalize across novel driving scenarios, handle long-tail edge cases, and improve continuously from large-scale fleet data. Our models jointly handle prediction and planning in a single unified architecture. Our stack is pure JAX end-to-end: the same code you train with is the code that runs on the robot. No C++ rewrites, no TensorRT export. A new policy goes from training to on-vehicle deployment in minutes.\n About the Role\n As a Senior/Staff Deep RL Engineer, you will design, train, and deploy deep reinforcement learning policies that make real-time driving decisions for our autonomous vehicles. You will own the full lifecycle, from problem formulation and reward design through large-scale distributed training to on-vehicle inference. You'll help define how learned components compose with the rest of the autonomy stack to produce robust, shippable behavior.\n You’re excited about this opportunity because you will… \n \n Formulate complex driving tasks as RL problems with well-shaped reward functions and expressive state/action representations.\n Design and train model-based deep RL agents using GPU-accelerated simulation at massive scale, including improving the simulator itself.\n Build and maintain distributed training infrastructure in JAX across large compute clusters.\n Build agentic optimization systems that automatically improve code, run experiments, analyze metrics, and iterate on RL policies with minimal human intervention.\n \n We’re excited about you because… \n \n BS/MS/PhD in CS, EE, Robotics, or a related field, with a strong foundation in reinforcement learning and deep learning.\n You have proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n Hands-on experience training RL agents at scale, ideally in robotics, autonomous driving, or other real-time decision-making domains.\n Proficiency in JAX or a similar functional ML framework; comfort with JIT compilation, vectorized environments, and GPU-accelerated simulation.\n Deep grasp of core RL concepts: policy gradients, value functions, exploration-exploitation, model-based RL, reward shaping, and sim-to-real transfer.\n Data-driven mindset: comfortable building experiment pipelines, analyzing training runs, and letting metrics guide architectural decisions.\n \n Nice to Have\n \n Publications at top venues (NeurIPS, ICML, ICLR, CoRL, RSS, ICRA) on RL or learned planning.\n Experience building or working with GPU-accelerated simulators for RL training.\n Track record of shipping a learned component in a production robotics or autonomous vehicle stack.\n \n  \n Notice Regarding Use of AI and Automated Tools:  To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem.\n How it works: Gem assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Gem or other AI-enabled tool  in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.\n Data Retention, Privacy \u0026 Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page . \n Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only\n We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.\n The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey \n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for ","salary_min":168000,"salary_max":247000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["robotics","cloud","healthcare","reinforcement-learning","jax","deep-learning","agents","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/7750664","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-25T21:23:08Z","expires_at":"2026-11-13T17:03:28.720972Z","created_at":"2026-04-17T04:55:26.063966Z","updated_at":"2026-10-14T17:03:28.724153Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/41e20e83-f1c0-412c-b84c-10a516f9fc81"},{"id":"3e244662-60ee-4bb5-aa88-87c510c965d1","company_id":"1dbb4356-f593-4e4f-a625-e241aa20b658","title":"Machine Learning Researcher","slug":"machine-learning-researcher-dfd1c7ec","description":"Virtu is a quantitative trading firm that uses cutting-edge models and infrastructure to provide liquidity to the global markets.\n As a Machine Learning Researcher at Virtu, you'll pursue high-impact research opportunities within a results-oriented, agile organization. This role offers the rare combination of intellectual challenge and direct business impact. You'll tackle complex problems without obvious solutions, taking ownership of our entire modeling ecosystem—from feature engineering and deep learning architecture design to training dynamics and execution strategy. Your innovations will directly influence how we operate in markets globally, making a tangible difference in a field that demands constant evolution, creative problem-solving, and first-principles thinking.\n A sense of curiosity, strong technical skillset, and collaborative mentality make you a good fit for this position, regardless of what industry you come from. \n The Role\n \n Investigate, evaluate, and prototype innovative algorithmic solutions using novel machine learning and deep learning techniques. Reinforcement learning experience is a bonus\n Results oriented mindset with a focus on developing deep learning models that directly impact P\u0026L\n Implement sophisticated ML approaches for forecasting, feature engineering, and optimization challenges\n Conduct empirical ML research across multiple problem domains, rapidly prototyping and iterating novel architectures in Python/PyTorch/TensorFlow to solve challenging market problems\n Apply logical and mathematical reasoning to translate cutting-edge research methods between application areas. Adapt techniques from your area of expertise to achieve breakthrough results in the financial markets\n Partner with quantitative traders, researchers, and developers across teams to transform market insights into actionable data features and predictive models\n \n  \n The Candidate\n \n Minimum 2 years of applied experience developing deep learning solutions across diverse fields\n Proven capability in applying machine learning methodologies between different problem domains and application areas\n Strong production mindset with emphasis on delivering solutions that create bottom-line value and tangible business outcomes\n Proficient in rapid prototyping and iterative development using Python and contemporary deep learning frameworks\n Advanced programming expertise in areas such as core PyTorch/JAX framework development. Exposure to C++ in production environments is a plus\n Comfortable partnering with other researchers, developers, and traders and working on cross-functional projects in a collaborative environment\n \n  \n Salary Range: $200,000 - $300,000 (salary range is exclusive of bonuses, benefits or other categories of compensation) \n Virtu Financial is an equal opportunity employer, committed to a diverse and inclusive workplace, welcoming you for who you are and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.","salary_min":200000,"salary_max":300000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["jax","deep-learning","tensorflow","reinforcement-learning","pytorch","machine-learning","research"],"apply_url":"https://job-boards.greenhouse.io/virtu/jobs/8477580002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-24T19:22:26Z","expires_at":"2026-11-13T17:02:55.462077Z","created_at":"2026-04-17T00:25:48.704181Z","updated_at":"2026-10-14T17:02:55.465239Z","company_name":"Virtu Financial","company_slug":"virtu-financial","company_logo_url":"https://www.google.com/s2/favicons?domain=virtu.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3e244662-60ee-4bb5-aa88-87c510c965d1"},{"id":"f47b2b52-9138-4056-a197-783873a96c39","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Software Engineer - Voice Model","slug":"member-of-technical-staff-voice-model-5b5f6cb9","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\n ABOUT THE ROLE \n You'll join the Grok Voice Model team as a software engineer. You'll design, build, and run the software systems behind Grok Voice, which delivers natural, low-latency, multilingual spoken interactions across devices and in real time. The team owns the full engineering stack behind the model, from large-scale data processing to distributed training to production deployment.\n RESPONSIBILITIES  \n \n Design, build, and maintain large-scale distributed data pipelines that ingest, process, and annotate real-world and synthetic audio, so the training and evaluation systems get reliable, high-quality data.\n Build and optimize the distributed training infrastructure and software for speech-language models in JAX and PyTorch, including the systems that run supervised fine-tuning and reinforcement learning at scale.\n Build automated evaluation and testing systems that measure accuracy, audio quality, latency, and real-time performance, plus the experimentation infrastructure used to validate every model release.\n Integrate voice models into production applications and real-time services, write the technical specifications for spoken interactions, and own deployment from prototype to global scale with a focus on stability, low latency, and reliability.\n Profile and optimize system performance across training and inference, including throughput, latency, memory use, and compute efficiency.\n \n BASIC QUALIFICATIONS  \n \n Bachelor's degree or higher in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related engineering field.\n Expert-level Python, writing clean, efficient, production-quality code for large-scale systems.\n Hands-on experience building distributed data processing systems with tools like Spark and Ray.\n Experience building and running large-scale distributed training and inference systems on Kubernetes.\n Experience implementing and optimizing model training and inference software in JAX or PyTorch.\n Experience building automated testing, evaluation, and A/B experimentation pipelines for production systems.\n A proactive, self-driven attitude and the ability to deliver in a fast-paced, high-caliber team.\n \n COMPENSATION AND BENEFITS:\n $150,000 - $450,000 USD\n Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n SpaceXSI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":150000,"salary_max":450000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["jax","fine-tuning","data-pipeline","pytorch","reinforcement-learning","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5051966007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-16T20:39:18Z","expires_at":"2026-11-13T16:55:31.950252Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-10-14T16:55:31.95433Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f47b2b52-9138-4056-a197-783873a96c39"},{"id":"2e69d116-6f10-4379-9eeb-e081c20eeea9","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer, Maneuvering Tech","slug":"senior-machine-learning-engineer-maneuvering-tech-465f6280","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Driver Refinement and Validation’s (DRV) mission is to build upon the high-quality ML Driver developed by Driver Intelligence, refining its performance to improve autonomous driving in specific, complex real-world contexts and problem areas (e.g., Ride-Hailing contexts). It aims to ensure that the Waymo Driver always computes safe, compliant, comfortable, and useful plans. As an Engineer in DRV, you will bridge the gap between cutting-edge machine learning and real-world vehicle performance, engineering the validation and onboard systems that ensure the autonomous vehicle reliably executes intelligent and predictable maneuvers.\n  \n You will: \n \n Develop next-generation, ML-powered systems that enhance the capabilities of the ML driver and accelerate the rapid scaling of Waymo’s business\n Fine-tune, adapt, and benchmark state-of-the-art generative models to improve autonomous vehicle perception, motion prediction, and trajectory planning\n Write high-quality, scalable, and thoroughly tested code to bring cutting-edge ML into production\n Partner with world-class engineers and product managers to deliver safe and smooth autonomous vehicle behaviors\n \n You have: \n \n MS in Computer Science, Machine Learning, Robotics, or related technical field, or equivalent practical experience\n 5+ years of industry experience deploying, evaluating, and maintaining ML-based systems in real-world, production environments\n Proficient programming skills in Python and/or C++, coupled with strong analytical and debugging abilities\n Expertise with modern deep learning frameworks, such as  JAX or PyTorch\n \n We prefer: \n \n Experience in deep learning, reinforcement learning, and ML systems\n Domain expertise in solving prediction, behaviors, motion planning, or related robotics problems\n ML post-training experience\n Prior industry experience (e.g. internships) in applied ML or software development\n Publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA,  IROS, RSS, CoRL, ACL, or EMNLP)\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","reinforcement-learning","jax","autonomous-vehicles","nlp","robotics","deep-learning","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7456936","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-12-15T16:37:54Z","expires_at":"2026-11-13T16:55:55.036256Z","created_at":"2026-10-14T15:20:04.475553Z","updated_at":"2026-10-14T16:55:55.039094Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2e69d116-6f10-4379-9eeb-e081c20eeea9"},{"id":"6097710f-699e-40f1-8353-7da141ef5213","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff Machine Learning Engineer, Infrastructure","slug":"senior-software-engineer-simulation-ml-infrastructure-a75506aa","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Join our ML Infrastructure engineering team advancing state-of-the-art ultra-realistic multi-agent simulations using foundation models. In this role, you will work at the intersection of ML infrastructure, foundation models, and simulation engineering, with a specific focus on writing high-performance business and simulation logic in JAX/TensorFlow running directly on TPUs to power realistic environments for Reinforcement Learning (RL).\n What You'll Do \n \n Design, build, and optimize realistic simulation environments and business logic running on TPUs using JAX and TensorFlow. Implement and optimize large-scale model and data parallelism strategies for training and running foundation models on TPU hardware.\n Collaborate closely with modeling teams to integrate foundation models into simulation pipelines.\n Drive technical architectures and system designs from data engineering through simulation execution to meet business and performance objectives.\n Profile systems, identify performance bottlenecks across ML accelerators, and optimize end-to-end execution speed.\n Translate product and business goals into concrete technical requirements and system deliverables.\n \n Minimum Qualifications \n \n 6+ years of professional software engineering experience, with at least 4 years focused on machine learning infrastructure (scaling, training, optimizing, and deploying large-scale ML systems).\n Direct ML programming experience on TPU and GPU hardware using frameworks such as JAX, PyTorch, or TensorFlow.\n Proven hands-on experience scaling large models using model parallelism, data parallelism, or distributed training techniques.\n Strong understanding of state-of-the-art ML models (e.g., autoregressive transformers) and hands-on proficiency with ML accelerator profiling tools to diagnose bottlenecks.\n Demonstrated ability to independently lead ambiguous technical initiatives end-to-end and build robust libraries, pipelines, and developer tooling.\n Strong verbal and written communication skills to collaborate effectively across distributed, cross-functional teams.\n \n Preferred Qualifications \n \n Practical experience in Reinforcement Learning (RL), Sim2Real transfer, or Robotics.\n Experience with distributed ML frameworks and accelerators like GPU/TPU.\n Domain familiarity with Autonomous Driving systems, multi-agent simulations, or realistic world modeling.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","distributed-systems","tensorflow","reinforcement-learning","autonomous-vehicles","jax","robotics","generative-ai"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7429718","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-12-08T21:56:12Z","expires_at":"2026-11-13T16:55:55.188802Z","created_at":"2026-04-13T09:40:18.543129Z","updated_at":"2026-10-14T16:55:55.192282Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6097710f-699e-40f1-8353-7da141ef5213"},{"id":"894372fd-9354-4dd5-94ad-9e9108537e4f","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Staff TLM, Machine Learning, Sentry Tower","slug":"senior-machine-learning-engineer-sentry-tower-430773b1","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n About the Team\n The Counter Intrusion MSE team develops systems that provide force protection capabilities, monitoring the perimeter of secure areas, land or sea, for approaching people, vehicles, and vessels. We live in a world where security officers are increasingly overwhelmed by sensor data feeds. Our products leverage advanced sensor fusion and autonomy to seamlessly render activity in the environment to Lattice's common operating picture. This relieves operators of our system from a ton of burden and allows our customers to get more done with fewer personnel. Increasingly, Counter Intrusion MLEs are working on projects to bring customers' existing legacy security systems into the Lattice ecosystem. Key to this effort is building scalable software solutions so that we can service many customers without requiring bespoke software for each one. The Counter Intrusion team is responsible for the development, testing, deployment, and sustainment of our family of systems. We work closely with other teams from product, engineering, sales, logistics, operations, and mission success.\n About the Role\n We are looking for a Staff ML Tech Lead Manager to apply the latest research to solve our toughest problems. This role will be responsible for owning the whole machine learning stack for the Counter Intrusion team. You will design and train multi-sensor object detection models for perception on edge compute devices that push the boundaries of what's possible with our sensors. You will develop learning algorithms to optimize the behavior of autonomous systems. You will also be responsible for the end-to-end design, implementation, and performance of the ML stack, including the infrastructure for data collection and training. Your work will directly impact Anduril's ability to deliver cutting-edge defense technology, with the opportunity to identify and develop novel ML applications across our product portfolio.\n Responsibilities\n \n Propose and prototype innovative solutions to solve real-world problems, leveraging the latest state-of-the-art techniques in the field\n Develop and maintain core ML pipelines\n Train and deploy deep learning models for real-time applications\n Collaborate cross-functionally with camera, systems and labeling teams\n Curate datasets for evaluating performance and comparing performance trends over time\n Provide technical mentorship to other junior ML engineers\n \n Required Qualifications\n \n MS or PhD in Machine Learning, Robotics or Computer Science, with emphasis on Computer Vision\n BS in Computer Science, Machine Learning, Electrical Engineering, or related field\n 6+ years of experience developing, benchmarking and optimizing ML algorithms on large-scale datasets\n Strong Deep Learning and CV background\n Proficiency in C++ development in a Linux environment\n Experience with Python development and deep learning frameworks such as PyTorch, JAX and TensorFlow\n Experience deploying models with TensorRT and ONNX\n Optimize on-device inference and vision kernels across CPU/GPU/NPU\n Track record of developing and deploying CV models from R\u0026D to production\n Experience writing and maintaining automated continuous integration tests\n Knowledge of system profiling and tuning for latency, memory and power efficiency\n Ability to conduct experiments, ablation studies and create highly detailed reports\n Eligible to obtain and maintain a U.S. Secret security clearance\n \n Preferred Qualifications\n \n Experience in one or more of the following:\n \n Object Detection, Object Tracking, Instance Segmentation, Semantic Segmentation, Semantic Change Detection, Depth Estimation, Model Pruning and Compression\n \n Experience in one or more of the following:\n \n Visual Odometry, SLAM, Multi-view Geometry, Camera Models, RGB-D and LIDAR Sensor Fusion, Optical Flow\n \n Experience troubleshooting and analyzing remotely deployed software systems\n 1 year of experience in a technical leadership role\n US Salary Range\n $220,000 — $330,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Hi","salary_min":220000,"salary_max":330000,"location":"Irvine, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["tensorflow","computer-vision","jax","pytorch","deep-learning","cloud","payments","robotics"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/4927589007?gh_jid=4927589007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-11-14T21:04:26Z","expires_at":"2026-11-13T16:57:16.772079Z","created_at":"2026-04-13T09:43:05.683344Z","updated_at":"2026-10-14T16:57:16.775Z","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/894372fd-9354-4dd5-94ad-9e9108537e4f"},{"id":"4fc83acb-2d08-48bd-b4a4-4d36a214db66","company_id":"84aa85d5-e0e2-4979-9998-322555a216c2","title":"Senior Backend Engineer, Data Modeling and Ingestion Platform","slug":"senior-backend-engineer-data-modeling-and-ingestion-platform-abaad41b","description":"About the Role \n We are looking for a Senior Backend Engineer to lead the unification of large, highly rich, and heterogeneous datasets sourced from a wide range of external providers. These datasets are used to power our generative audio models. \n Your work will create the foundational dataset that powers our research by building robust, scalable systems for linking, deduplicating, reconciling, and enriching data at massive scale. This role centers on  high-impact bulk ingestion and advanced data linkage . You will design the logic, algorithms, and strategies that transform many independent datasets into a unified, high-quality canonical asset used throughout the company.\n You will collaborate closely with ML researchers and product teams, working with tools such as BigQuery, Dataflow/Beam, TFRecords , and—where beneficial—distributed systems frameworks like  Ray . Familiarity with ML workflows using  JAX or multihost training is a plus, as the datasets you produce will directly support that ecosystem.\n What You'll Do\n \n Build high-throughput  bulk ingestion workflows  to integrate datasets from multiple external providers. \n Design and implement scalable  entity-resolution  solutions, including record linking, deduplication, clustering, and conflict arbitration. \n Create and refine  matching logic, decision rules, and similarity functions  to align datasets with high accuracy and strong coverage. \n Define and track  data quality indicators , such as overlap metrics, match precision/recall, duplicate rates, and completeness. \n Prepare training-ready datasets in formats such as  TFRecords , and structure data to meet ML research requirements. \n Develop processing components using  Dataflow (Beam) and manage large analytical workloads in BigQuery . \n Leverage frameworks like  Ray  to accelerate large-scale experiments, feature extraction, and research-oriented data preparation. \n Collaborate with ML researchers to anticipate downstream requirements and evolve linkage strategies as new sources and use cases emerge. \n \n What We're Looking For \n \n Experience working with  large, heterogeneous datasets from multiple providers or domains. \n Strong background in  entity resolution , deduplication, data unification, or related large-scale data integration techniques. \n Proficiency in  Python , with an emphasis on efficient, scalable data processing. \n Experience with  BigQuery, Google Dataflow/Apache Beam , or similar batch-processing frameworks. \n Familiarity with  data validation, normalization, reconciliation , and building consistent views across diverse data sources. \n Ability to craft well-structured  matching and decision strategies  that balance accuracy, completeness, and computational efficiency. \n Comfortable iterating quickly on pragmatic solutions, balancing correctness with time-to-delivery. \n Clear communication skills and the ability to collaborate closely with ML and research teams. \n \n  Nice to Have\n \n Knowledge of architecting Google Cloud Platform systems at scale\n Experience with distributed compute frameworks such as Ray , Spark , or Flink . \n Understanding of  JAX-based ML pipelines ,  multihost training setups,  or large-scale data preparation for accelerator-backed workflows. \n Familiarity with  TFRecords  or other high-volume training data formats. \n Exposure to ranking, clustering, or statistical similarity modeling. \n Experience with Go , NextJS , and/or React Native to contribute to full-stack development\n \n Why Join Us \n \n You will design the  core dataset  that underpins our research, product development, and generative audio models. \n You'll work on large-scale data challenges that require creativity, algorithmic thinking, and engineering excellence.\n You'll join a small, fast-moving team where your decisions shape the direction of our data and research capabilities.\n \n Benefits\n \n Highly competitive salary and equity \n Quarterly productivity budget\n Flexible time off\n Fantastic office location in Manhattan\n Productivity package, including ChatGPT Plus, Claude Code, and Copilot\n Top notch private health, dental, and vision insurance for you and your dependents\n 401(k) plan options with employer matching \n Concierge medical/primary care through One Medical and Rightway \n Mental health support from Spring Health \n Personalized life insurance, travel assistance, and many other perks\n \n Udio’s success hinges on hiring great people and creating an environment where we can be happy, feel challenged, and do our best work. \n Udio provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.\n This role is eligible for a c","salary_min":180000,"salary_max":220000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["code-generation","jax","distributed-systems","backend"],"apply_url":"https://job-boards.greenhouse.io/udio/jobs/4988140008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-11-13T21:43:53Z","expires_at":"2026-11-13T17:02:58.038341Z","created_at":"2026-04-17T00:48:46.067742Z","updated_at":"2026-10-14T17:02:58.041321Z","company_name":"Udio","company_slug":"udio","company_logo_url":"https://www.google.com/s2/favicons?domain=udio.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4fc83acb-2d08-48bd-b4a4-4d36a214db66"},{"id":"032f8e67-5013-41ef-9669-36c185ec15ba","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Machine Learning Engineer / Applied Scientist, Prediction \u0026 Planning ","slug":"research-scientist-prediction-planning-a4891add","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently.\n You will: \n \n Design, implement, and evaluate state-of-the-art generative models for autonomous vehicle planning and prediction\n Develop next-generation, ML-powered systems that enhance the capabilities of the ML driver and accelerate the rapid scaling of Waymo’s business\n Translate open-ended, real-world driving challenges into well-defined machine learning problems, applying cutting-edge techniques, including foundation models and reinforcement learning\n Write high-quality, scalable, and thoroughly tested code to bring cutting-edge research into production\n Partner with world-class researchers, engineers, and product managers to deliver safe and smooth planning behaviors, and publish findings at top-tier academic venues\n \n You have: \n \n PhD in Computer Science, Machine Learning, Robotics, a related technical field, or equivalent practical experience\n A proven track record of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA,  IROS, RSS, CoRL, ACL, or EMNLP)\n Demonstrated impact on the broader ML community through influential research, widely adopted open-source projects, or significant industry contributions\n Hands-on expertise with modern deep learning frameworks, for example JAX or PyTorch\n Proficient programming skills in Python and/or C++, coupled with strong analytical and debugging abilities\n \n We prefer: \n \n Specialized research experience in deep learning, reinforcement learning, causal reasoning, or foundation models\n Prior industry experience (e.g. internships) in applied ML research or software development\n Domain expertise in solving motion planning, prediction, or related robotics problems\n Hands-on experience deploying, evaluating, and maintaining ML-based systems in real-world, production environments\n \n #LI-Hybrid\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["jax","robotics","nlp","reinforcement-learning","deep-learning","autonomous-vehicles","pytorch","generative-ai"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7309064","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-10-08T19:48:21Z","expires_at":"2026-11-13T16:55:54.929528Z","created_at":"2026-04-13T09:40:16.579726Z","updated_at":"2026-10-14T16:55:54.93231Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/032f8e67-5013-41ef-9669-36c185ec15ba"},{"id":"2ece4fd6-b509-4c91-9d08-6737394ecee8","company_id":"a0000000-0000-0000-0000-000000000001","title":"Performance Engineer, GPU","slug":"performance-engineer-gpu-f497fb8a","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  \n About the role:\n Pioneering the next generation of AI requires breakthrough innovations in GPU performance and systems engineering. As a GPU Performance Engineer, you'll architect and implement the foundational systems that power Claude and push the frontiers of what's possible with large language models. You'll be responsible for maximizing GPU utilization and performance at unprecedented scale, developing cutting-edge optimizations that directly enable new model capabilities and dramatically improve inference efficiency.\n Working at the intersection of hardware and software, you'll implement state-of-the-art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack—from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.\n Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.\n You might be a good fit if you:\n \n Have deep experience with GPU programming and optimization at scale\n Are impact-driven, passionate about delivering measurable performance breakthroughs\n Can navigate complex systems from hardware interfaces to high-level ML frameworks\n Enjoy collaborative problem-solving and pair programming\n Want to work on state-of-the-art language models with real-world impact\n Care about the societal impacts of your work\n Thrive in ambiguous environments where you define the path forward\n \n Strong candidates may also have experience with:\n \n GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization\n ML Compilers \u0026 Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators\n Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight\n Distributed Systems: NCCL, NVLink, collective communication, model parallelism\n Low-Precision: INT8/FP8 quantization, mixed-precision techniques\n Production Systems: Large-scale training infrastructure, fault tolerance, cluster orchestration\n \n Representative projects:\n \n Co-design attention mechanisms and algorithms for next-generation hardware architectures\n Develop custom kernels for emerging quantization formats and mixed-precision techniques\n Design distributed communication strategies for multi-node GPU clusters\n Optimize end-to-end training and inference pipelines for frontier language models\n Build performance modeling frameworks to predict and optimize GPU utilization\n Implement kernel fusion strategies to minimize memory bandwidth bottlenecks\n Create resilient systems for planet-scale distributed training infrastructure\n Profile and eliminate performance bottlenecks in production serving infrastructure\n Partner with hardware vendors to influence future accelerator capabilities and software stacks\n \n  \n Deadline to apply: None. Applications will be reviewed on a rolling basis. \n  \n The expected salary range for this position is:\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 $280,000 — $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 ","salary_min":280000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["llm","pytorch","distributed-systems","jax","gpu","alignment","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/4926227008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-09-22T16:25:42Z","expires_at":"2026-11-13T16:54:55.909761Z","created_at":"2026-04-13T09:35:57.775908Z","updated_at":"2026-10-14T16:54:55.912838Z","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/2ece4fd6-b509-4c91-9d08-6737394ecee8"},{"id":"dece2ce5-bcc8-4aa1-9ca3-8dd4ad056b38","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Machine Learning Engineer - Recommendation Systems","slug":"member-of-technical-staff-recommendation-systems-f9142f82","description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\n ABOUT THE ROLE: \n We’re seeking exceptional Applied engineers to join a high-priority project that approximately 600 million monthly users use. This is an exciting opportunity for individuals with a engineer or scientist background to apply their skills to recommendation systems, ranking algorithms, search technologies, and many other systems. You’ll work at the intersection of advanced AI development and real-world impact, enhancing the ability to connect users with relevant content, accounts, and experiences.\n RESPONSIBILITIES: \n \n Designing and architecting recommendation algorithms across various product surfaces\n Leverage all of SpaceXSI's infra and AI stacks to dramatically enhance the user experience\n Write data pipelines and training jobs that continuously learn from product data.\n Iterate and improve the algorithm by gathering user feedback in real time through experimentation\n Ensuring scalability and efficiency of machine learning systems\n \n BASIC QUALIFICATIONS: \n \n Knowledge of data infrastructure like Kafka, Clickhouse, and Spark\n Experienced in implementing recommender systems and/or deep learning applications at industrial scale\n Skilled in one or more DL software frameworks such as JAX or PyTorch\n Exceptional candidates may be experienced in writing CUDA kernels\n \n COMPENSATION AND BENEFITS: \n $180,000 - $440,000 USD\n Base salary is just one part of our total rewards package at SpaceXSI , which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n SpaceXSI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","data-pipeline","pytorch","jax","gpu","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/4703144007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-04-11T03:21:31Z","expires_at":"2026-11-13T16:55:31.634517Z","created_at":"2026-04-13T09:38:43.081285Z","updated_at":"2026-10-14T16:55:31.637082Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dece2ce5-bcc8-4aa1-9ca3-8dd4ad056b38"},{"id":"d527ce1c-d2ec-4527-8e61-0ff4242b0f65","company_id":"4c0fefc3-173a-4227-a823-4d67d3e70ff0","title":"Research Scientist, Climate Modeling ","slug":"research-scientist-climate-modeling-499012d5","description":"Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.\n Our base salary range is $167,030 - $260,570, and in addition we have generous bonus plans to provide a competitive compensation package.  \n We will begin reviewing applications on October 19, 2026. \n Who You Are: \n We seek one or more Research Scientists for a new three-year project to develop an AI-powered physics-informed hybrid climate model trained on historical data that is capable of multidecadal forecasts that are more accurate than the current state of the art. We’re looking for someone with experience in AI and earth science who is excited to push the frontier in climate model development.   \n This position is supported by a grant with a current period of performance ending September 22, 2029. Continuation of the position beyond that date depends on renewal, extension, or identification of additional funding. Ai2 will make reasonable efforts to notify the employee if funding for the position is expected to end. \n Who We Are:  \n Ai2’s Climate Modeling team is an international pioneer in using machine learning (ML) methodologies to improve on current physics-based climate models.  Our fast, accurate autoregressive open-source emulator, ACE, stably reproduces the climate and weather extremes of an existing climate model or a reanalysis. We have coupled ACE to a ML ocean model, trained it to  realistically account  for changing CO2 concentrations, and downscaled (super-resolved) ACE outputs from their 100 km native scale to to km-scale detail. Our tight-knit team of scientists and software engineers works closely with leading physics-based climate modeling centers to obtain unique training and testing data and get expert feedback on our progress. We collaborate with other leading research groups doing related AI work.\n Your Next Challenge: \n Physically-based climate models discretize equations representing individual time-evolving processes like clouds, rain, wind, land and sea-ice, and ocean currents on a computational grid.  Some processes (e.g. clouds) are less reliably encoded than others (e.g. winds).  Hence such climate models have biases in representing present-day climate and produce an undesirably large range of projections of future climate change for a given human-caused change in CO2 or other climate forcings.  Current AI-based climate models reduce present-day climate bias, but don’t generalize well to future climate change.\n We are starting a 3-year project funded by google.org  to develop an open source ‘AI-first’ climate model that can demonstrably project future climates more accurately than current physically-based climate models when trained on historical observations by judicious design of the AI to incorporate physical principles that reliably apply in any climate, seen or unseen.   The model should be lightweight and easy for an ML-savvy climate scientist to train and deploy. Our starting point is the Google Research NeuralGCM model, a hybrid ML model that uses a conventional discretization of winds encoded in JAX and learns a column-local representation of all other atmospheric and land processes from historical data. Compared to other AI climate models, NeuralGCM uses ML in a conceptually simpler way and shows somewhat better future-climate generalization skill.   It is an open-weight model but its training pipeline  and documentation need to be adapted for a broader user community.\n You will join a small team to develop the desired NeuralGCM+ model and foster its uptake by the climate modeling community.  This includes coupling it to an ML-based ocean model and designing approaches to demonstrably improve its skill in unseen climates.  This position has a three year term; there is potential for longer-term funding if  the NeuralGCM+ project demonstrates exceptional progress.\n You'll get to work on:\n \n Designing, implementing and testing a strategy for coupling NeuralGCM with the chosen ocean model.  Note that NeuralGCM is written in JAX and implemented on TPUs.\n Designing, implementing and testing a strategy for decomposing physical parameterizations and constraints  into parts encoded based on physical equations vs. parts that can be learned (even for unseen climates) from historical observations.\n Presenting results at major relevant AI and climate modeling conferences and in peer-reviewed publications.\n Open-source code development as part of a tightly connected team, including daily meetings, code review, design documents and interaction with external collaborators.  \n Mentoring postdocs and interns.\n \n What You’ll Need: \n \n You must have substantial experience building and deploying a relevant ML application in a practical or academic research setting and be fluent in Python and collaborative coding practices.\n You mus","salary_min":167030,"salary_max":260570,"location":"Seattle, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["robotics","jax","research"],"apply_url":"https://job-boards.greenhouse.io/thealleninstitute/jobs/8234335","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T21:43:18Z","expires_at":"2026-11-13T17:02:42.391747Z","created_at":"2026-10-14T15:24:45.323459Z","updated_at":"2026-10-14T17:02:42.396227Z","company_name":"Allen Institute for AI","company_slug":"allen-institute-for-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=allenai.org\u0026sz=128","quality_score":85,"url":"https://aidevboard.com/job/d527ce1c-d2ec-4527-8e61-0ff4242b0f65"}],"page":1,"per_page":20,"total":44,"total_is_exact":true,"total_pages":3}