{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \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 xAI, 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 SpaceXAI 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":["deep-learning","pytorch","gpu","jax","distributed-systems","data-pipeline","machine-learning","infrastructure"],"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":"2026-09-29T13:33:49.819622Z","created_at":"2026-07-22T14:03:40.513428Z","updated_at":"2026-08-30T13:33:49.955116Z","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":["mlops","pytorch","jax","computer-graphics","generative-ai","llm","gpu"],"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":"2026-09-29T13:35:28.56682Z","created_at":"2026-07-15T14:06:51.909822Z","updated_at":"2026-08-30T13:35:28.702215Z","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":"dcbc3e4a-5095-4162-b887-e85c446bc016","company_id":"4ed3e523-b627-46ed-8dae-04c2ea823be2","title":"Research Engineer, Pre-Training","slug":"research-engineer-pre-training-10faf296","description":"Jump Trading Group is committed to world-class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk-adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.\n Our team is a group of quantitative researchers, engineers, and ML experts leading foundation model research and trading at Jump. Our mission is to combine emerging techniques and original research to generate signals from financial market data and monetize it globally. We are building the future of ML-powered trading through breakthrough foundation models, and we're looking for an exceptional Pre-Training Engineer to join our team.\n What You'll Do: \n As a Pre-Training Research Engineer, you'll be at the forefront of developing massive-scale foundation models that fundamentally transform how we understand and predict markets. You'll own and drive the entire training stack: building fault-tolerant infrastructure that scales across thousands of GPUs and TPUs with near-linear performance, engineering data pipelines that stream terabytes per second as our models train on petabytes of data from every corner of the global markets, and designing custom kernels that unlock 10x efficiency gains. Co-designing novel architectures with researchers and pioneering cutting-edge approaches to mixed-precision training and model parallelism, you'll have the latest generation hardware at your disposal. This isn't incremental optimization; we're pushing the boundaries of what's possible in pre-training at scale, where your improvements directly impact live trading.\n Other duties as assigned or needed.\n Skills You'll Need: \n \n Expertise and track record of significant, measurable performance improvements in large-scale distributed training (MFU, throughput, convergence, cost-per-token).\n Published research in efficient training methods, scaling laws, architectures, or systems for ML\n Background in numerical computing, HPC, or distributed systems, including familiarity with GPUs/TPUs, high-performance networking (NVLink/InfiniBand), Kubernetes/Slurm, and OS internals\n Expertise in Python and deep experience with modern deep learning frameworks (PyTorch and/or JAX)\n Advanced degree (MS or PhD) in Computer Science, Machine Learning, Physics, Mathematics, or a related quantitative field, or equivalent industry experience at a frontier lab\n Ability to balance ambitious research goals with practical engineering constraints\n Strong problem-solving skills, results orientation, and excellent collaborative communication\n Reliable and predictable availability\n \n Bonus Points: \n \n Expertise in: CUDA kernel development, Triton/Pallas/CuTe DSLs, PyTorch/JAX internals, XLA optimization, or hardware acceleration (FPGA/ASIC)\n Knowledge of reinforcement learning, post-training, or fine-tuning techniques\n Knowledge of financial markets or trading\n Benefits \n \n Discretionary bonus eligibility \n Medical, dental, and vision insurance \n HSA, FSA, and Dependent Care options \n Employer Paid Group Term Life and AD\u0026D Insurance \n Voluntary Life \u0026 AD\u0026D insurance \n Paid vacation plus paid holidays \n Retirement plan with employer match \n Paid parental leave \n Wellness Programs \n \n Annual Base Salary Range \n $300,000 — $350,000 USD","salary_min":300000,"salary_max":350000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["search","pytorch","fine-tuning","jax","reinforcement-learning","distributed-systems","generative-ai","data-pipeline"],"apply_url":"https://www.jumptrading.com/hr/job?gh_jid=7977686","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-02T21:37:37Z","expires_at":"2026-09-29T13:47:31.273506Z","created_at":"2026-06-28T14:16:33.258804Z","updated_at":"2026-08-30T13:47:31.41462Z","company_name":"Jump Trading","company_slug":"jump-trading","company_logo_url":"https://www.google.com/s2/favicons?domain=jumptrading.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dcbc3e4a-5095-4162-b887-e85c446bc016"},{"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":"SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.  Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \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 SpaceXAI, 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 SpaceXAI is an equal opportunity employer. For details on data processing, view our R","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","agents","data-pipeline","jax","pre-training","generative-ai","llm","reinforcement-learning"],"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":"2026-09-29T13:33:49.168729Z","created_at":"2026-04-17T19:30:23.022034Z","updated_at":"2026-08-30T13:33:49.307041Z","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":["mlops","gpu","search","pytorch","llm","jax","data-pipeline","distributed-systems"],"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":"2026-09-29T13:44:53.533476Z","created_at":"2026-04-16T18:53:13.262577Z","updated_at":"2026-08-30T13:44:53.66309Z","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":["jax","reinforcement-learning","fine-tuning","distributed-systems","healthcare","robotics","deep-learning","autonomous-vehicles"],"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":"2026-09-29T13:49:20.564062Z","created_at":"2026-04-17T04:55:26.063966Z","updated_at":"2026-08-30T13:49:20.694673Z","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":["tensorflow","deep-learning","reinforcement-learning","pytorch","jax","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":"2026-09-29T13:47:59.960935Z","created_at":"2026-04-17T00:25:48.704181Z","updated_at":"2026-08-30T13:48:00.090634Z","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":"6097710f-699e-40f1-8353-7da141ef5213","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior/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","generative-ai","autonomous-vehicles","tensorflow","robotics","reinforcement-learning","jax","distributed-systems"],"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":"2026-09-29T13:35:08.431022Z","created_at":"2026-04-13T09:40:18.543129Z","updated_at":"2026-08-30T13:35:08.567996Z","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":"Senior Machine Learning Engineer, 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 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 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. Hig","salary_min":220000,"salary_max":330000,"location":"Irvine, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["tensorflow","pytorch","jax","computer-vision","payments","robotics","deep-learning","cloud"],"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":"2026-09-29T13:37:22.34712Z","created_at":"2026-04-13T09:43:05.683344Z","updated_at":"2026-08-30T13:37:22.481421Z","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":["distributed-systems","jax","code-generation","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":"2026-09-29T13:48:04.50853Z","created_at":"2026-04-17T00:48:46.067742Z","updated_at":"2026-08-30T13:48:04.640354Z","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":["robotics","pytorch","generative-ai","autonomous-vehicles","jax","deep-learning","nlp","reinforcement-learning"],"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":"2026-09-29T13:35:04.002479Z","created_at":"2026-04-13T09:40:16.579726Z","updated_at":"2026-08-30T13:35:04.135924Z","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":"fa9d79c2-43c3-4c01-87ae-7394dedbc4d1","company_id":"380257a2-8ce4-4838-adfe-843f5e9cf8c7","title":"Member of Technical Staff, AI Agent Development Lead","slug":"member-of-technical-staff-ai-agent-development-lead-29b9036b","description":"Who Are We? \n Postman is the world’s leading API platform, used by more than 45 million+ developers and 500,000 organizations, including 98% of the Fortune 500. Postman is helping developers and professionals across the globe build the API-first world by simplifying each step of the API lifecycle and streamlining collaboration—enabling users to create better APIs, faster.\n The company is headquartered in San Francisco and has offices in Boston, New York, Austin, Tokyo, London, and Bangalore - where Postman was founded. Postman is privately held, with funding from Battery Ventures, BOND, Coatue, CRV, Insight Partners, and Nexus Venture Partners. Learn more at postman.com or connect with Postman on X via @getpostman.\n P.S: We highly recommend reading The \"API-First World\" graphic novel to understand the bigger picture and our vision at Postman.\n The Opportunity \n As a Member of Technical Staff and AI Agent Development Lead, you will lead the design, development, and deployment of next-generation AI agents that interact with users and complex environments. You will drive the architecture and implementation of scalable, reliable AI systems, working closely with research, product and engineering teams to build safe, interpretable, and performant AI technology.\n What You’ll Do \n \n \n Lead a cross-functional engineering team focused on AI agent development, from conceptual design to production deployment.\n \n Design and implement AI agent architectures leveraging state-of-the-art language models and associated technologies.\n \n Collaborate with research scientists on scalable experiments and productize research innovations.\n \n Drive the development of agent capabilities including dialogue management, decision making, and autonomy.\n \n Ensure AI safety and alignment principles are integrated throughout the agent lifecycle.\n \n Mentor and grow technical staff, fostering an environment of collaboration and innovation.\n \n Evaluate new tools, frameworks, and methodologies to enhance AI agent capabilities.\n \n Partner with product and policy teams to align AI agent features with user needs and ethical standards.\n \n About You \n \n \n Proven experience leading technical teams in AI or machine learning engineering, preferably building AI agents or assistants.\n \n Strong software engineering skills with proficiency in Python and familiarity with modern ML frameworks (e.g., JAX , PyTorch, TensorFlow).\n \n Deep understanding of language models, reinforcement learning, and AI safety/alignment concepts.\n \n Experience with scalable system design and cloud infrastructure.\n \n Passion for AI safety, interpretability, and user-centered AI development.\n \n Excellent communication skills and ability to collaborate across multidisciplinary teams.\n \n Preferred :\n \n \n Prior experience working with large language models or conversational AI.\n \n Background in research and development of AI alignment or safety techniques.\n \n Experience with developer tools and APIs for AI integration.\n \n The reasonably estimated base salary for this role ranges from $256,000 to $276,000, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. \n What Else? \n In addition to Postman's pay-on-performance philosophy, and a flexible schedule working with a fun, collaborative team, Postman offers a comprehensive set of benefits, including full medical coverage, flexible PTO, wellness reimbursement, and a monthly lunch stipend. Along with that, our wellness programs will help you stay in the best of your physical and mental health. Our frequent and fascinating team-building events will keep you connected, while our donation-matching program can support the causes you care about. We’re building a long-term company with an inclusive culture where everyone can be the best version of themselves. \n At Postman we value in person collaboration. We are in office 5 days a week for all roles based out of our hubs in San Francisco Bay Area, Boston, Austin, New York City, Tokyo and London. For roles based in Bangalore, employees currently work in the office three days a week and will transition to five days per week by the end of the year. We were thoughtful in our approach which is based on collaboration and grounded in feedback from our workforce, leadership team, and peers. The benefits of our in office model will be shared knowledge, brainstorming sessions, communication, and building trust in-person that cannot be replicated via zoom.\n Our Values \n At Postman, we create with the same curiosity that we see in our users. We value transparency and honest communication about not only successes, but also failures. In our work, we focus on specific goals that add up to a larger vision. Our inclusive work culture ensures that everyone is valued equally as important pieces of our final product. We are dedicated to delivering the best products we can.\n Equal opportunity \n Postman is an Equal Employment Opportunity a","salary_min":256000,"salary_max":276000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","tensorflow","reinforcement-learning","jax","alignment","agents","llm","cloud"],"apply_url":"https://job-boards.greenhouse.io/postman/jobs/7452542003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-10-08T17:49:47Z","expires_at":"2026-09-29T13:49:06.237029Z","created_at":"2026-04-17T04:55:11.806188Z","updated_at":"2026-08-30T13:49:06.36811Z","company_name":"Postman","company_slug":"postman","company_logo_url":"https://www.google.com/s2/favicons?domain=postman.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fa9d79c2-43c3-4c01-87ae-7394dedbc4d1"},{"id":"13d9c5e4-026e-4760-b7d6-4dbd59358b88","company_id":"380257a2-8ce4-4838-adfe-843f5e9cf8c7","title":"Applied AI Scientist, Small Language Model and AI Training","slug":"applied-ai-scientist-small-language-model-and-ai-training-cf1d4027","description":"Who Are We? \n Postman is the world’s leading API platform, used by more than 45 million+ developers and 500,000 organizations, including 98% of the Fortune 500. Postman is helping developers and professionals across the globe build the API-first world by simplifying each step of the API lifecycle and streamlining collaboration—enabling users to create better APIs, faster.\n The company is headquartered in San Francisco and has offices in Boston, New York, Austin, Tokyo, London, and Bangalore - where Postman was founded. Postman is privately held, with funding from Battery Ventures, BOND, Coatue, CRV, Insight Partners, and Nexus Venture Partners. Learn more at postman.com or connect with Postman on X via @getpostman.\n P.S: We highly recommend reading The \"API-First World\" graphic novel to understand the bigger picture and our vision at Postman.\n The Opportunity \n As an Applied Scientist specializing in Small Language Models and AI Training, you will lead research and development efforts focused on building efficient, high-performance language models tailored for practical applications. You will work closely with research, engineering, and product teams to advance model training techniques, optimize architectures, and scale AI solutions. Your work will directly contribute to AI systems that are safe, interpretable, and impactful across diverse usage scenarios.\n What You’ll Do \n \n \n Lead research and development of novel training methodologies and architectures for small and efficient language models.\n \n Design, implement, and evaluate model training experiments to improve performance, robustness, and generalization of language models.\n \n Collaborate closely with research scientists and engineers on scalable training pipelines and model deployment strategies.\n \n Develop techniques for model compression, fine-tuning, and domain adaptation to optimize models for real-world applications.\n \n Ensure AI safety, fairness, and alignment principles are integrated into model training processes and evaluated rigorously.\n \n Mentor and support cross-functional teams on applied machine learning methods and best practices.\n \n Evaluate and integrate new tools, frameworks, and datasets to accelerate AI training workflows.\n \n Partner with product teams to translate model capabilities into actionable features aligned with user needs and ethical standards.\n \n About You \n \n \n Have demonstrated experience in applied research or engineering roles focused on training language models, ideally small or efficient models.\n \n Strong programming skills in Python and familiarity with machine learning frameworks such as PyTorch, TensorFlow, or JAX .\n \n Deep understanding of language model architectures, training techniques, and optimization strategies.\n \n Experience with distributed training, data pipeline design, and scalable AI infrastructure.\n \n Passion for AI safety, interpretability, and delivering user-centered AI technology.\n \n Excellent communication skills with proven ability to collaborate across research, engineering, and product teams.\n \n Preferred\n \n \n Prior experience working with large and small language models in production or research settings.\n \n Background in reinforcement learning, prompt engineering, or transfer learning techniques.\n \n Experience with developer tools, APIs, or frameworks related to AI model integration and delivery.\n \n Knowledge of AI alignment, fairness, and ethical AI training methodologies.\n \n The reasonably estimated base salary for this role ranges from $218,500.00 to $288,000.00, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. \n What Else? \n In addition to Postman's pay-on-performance philosophy, and a flexible schedule working with a fun, collaborative team, Postman offers a comprehensive set of benefits, including full medical coverage, flexible PTO, wellness reimbursement, and a monthly lunch stipend. Along with that, our wellness programs will help you stay in the best of your physical and mental health. Our frequent and fascinating team-building events will keep you connected, while our donation-matching program can support the causes you care about. We’re building a long-term company with an inclusive culture where everyone can be the best version of themselves. \n At Postman we value in person collaboration. We are in office 5 days a week for all roles based out of our hubs in San Francisco Bay Area, Boston, Austin, New York City, Tokyo and London. For roles based in Bangalore, employees currently work in the office three days a week and will transition to five days per week by the end of the year. We were thoughtful in our approach which is based on collaboration and grounded in feedback from our workforce, leadership team, and peers. The benefits of our in office model will be shared knowledge, brainstorming sessions, communication, and building trust in-person that cannot be replicated via zoom.\n Our Values","salary_min":218500,"salary_max":288000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","mlops","pytorch","fine-tuning","alignment","jax","reinforcement-learning","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/postman/jobs/7452539003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-10-08T17:02:29Z","expires_at":"2026-09-29T13:49:06.010176Z","created_at":"2026-04-17T04:55:11.631102Z","updated_at":"2026-08-30T13:49:06.178176Z","company_name":"Postman","company_slug":"postman","company_logo_url":"https://www.google.com/s2/favicons?domain=postman.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/13d9c5e4-026e-4760-b7d6-4dbd59358b88"},{"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":["pytorch","gpu","alignment","jax","llm","distributed-systems","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":"2026-09-29T13:30:24.88021Z","created_at":"2026-04-13T09:35:57.775908Z","updated_at":"2026-08-30T13:30:25.028791Z","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":"SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.  Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \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 SpaceXAI'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 SpaceXAI, 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 SpaceXAI 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":["gpu","data-pipeline","deep-learning","pytorch","jax","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":"2026-09-29T13:33:48.064972Z","created_at":"2026-04-13T09:38:43.081285Z","updated_at":"2026-08-30T13:33:48.197754Z","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":"30c90b0d-0f42-411e-89c2-0a550ddbcfe9","company_id":"10df5a0b-95c8-4a89-a3a6-af92f87992f5","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-e3dd80a9","description":"About this opportunity: \n At Freenome, we are seeking a Senior Machine Learning Research Engineer to join the Machine Learning Science (MLS) team, within the Computational Science department. The ideal candidate has a strong knowledge in designing and building deep learning (DL) pipelines, and expertise in creating reliable, scalable artificial intelligence/machine learning (AI/ML) systems in a cloud environment.  \n The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimizing hardware utilization for efficient training, and performing model optimizations. As part of an interdisciplinary R\u0026D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission of reducing cancer mortality via accessible early detection.  \n The role reports to the Director of Machine Learning Science. This can be a hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote. \n What you’ll do: \n \n Implement and refine DL pipelines on distributed computing platforms enhancing the speed and efficiency of DL operations including model training, data handling, model management, and inference. \n Collaborate closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines you create are perfectly aligned with scientific goals and operational needs. \n Continuously monitor, evaluate, and optimize DL model training pipelines for performance and scalability. \n Stay up to date with the latest advancements in AI, ML, and related technologies, and quickly learn and adapt new tools and frameworks, if necessary. \n Develop and maintain robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results. \n Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation pipelines. \n Act as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement. \n \n Must haves: \n \n MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development.  \n 5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines. \n Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc. \n Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn. \n In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow.  \n Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment. \n Understanding of containerization technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions. \n Proven track record of developing and optimizing workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume. \n Experience managing large datasets, including data storage (eg: HDFS or Parquet on object storage), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark). \n Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows. \n Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team. \n Excellent ability to work effectively with cross-functional teams and communicate across disciplines.  \n \n Nice to haves: \n \n Experience working with large-scale genomics or biological datasets.  \n Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data. \n Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA). \n Experience with infrastructure-as-code and configuration management. \n Experience cultivating MLOps and ML infr","salary_min":173775,"salary_max":246750,"location":"Remote","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["jax","mlops","distributed-systems","tensorflow","gpu","llm","deep-learning","search"],"apply_url":"https://job-boards.greenhouse.io/freenome/jobs/8535248002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-16T21:23:48Z","expires_at":"2026-09-29T13:44:36.753277Z","created_at":"2026-07-18T14:15:34.953947Z","updated_at":"2026-08-30T13:44:36.881156Z","company_name":"Freenome","company_slug":"freenome","company_logo_url":"https://www.google.com/s2/favicons?domain=freenome.com\u0026sz=128","quality_score":85,"url":"https://aidevboard.com/job/30c90b0d-0f42-411e-89c2-0a550ddbcfe9"},{"id":"a7e6ada4-4484-41e6-91b8-e34294561d01","company_id":"a0b04b48-9259-414d-93bd-ae677520bef1","title":"Software Engineer - Triton","slug":"software-engineer-triton-5daaeaf7","description":"About the job\n Build the framework support that helps AI developers unlock Graphcore hardware.\n You will help Graphcore accelerators work seamlessly with state-of-the-art ML frameworks, including Triton and PyTorch. Reporting to a Team Lead in Frameworks, you will design, implement, optimise and support critical software.\n Your work will shape how researchers and engineers use Graphcore hardware for demanding machine learning workloads. You will improve performance, quality and usability across a complex software stack.\n You will join our Triton team, delivering new features, reviewing code, writing technical documentation and working with upstream communities. You will also help coordinate open-ended technical work across teams.\n This role offers deep technical ownership and visible impact as the ML software landscape evolves. You will keep learning and may contribute across other framework teams.\n The team and culture\n The Frameworks team connects Graphcore hardware with the tools ML engineers and researchers rely on. We work across PyTorch, Triton, JAX and TensorFlow, focusing on usability and performance.\n Work happens through agile practices, close technical discussion and clear ownership. Engineers are trusted to speak up, solve hard problems and move work forward with pace.\n Decisions are shaped by evidence, code quality and business outcomes. You will work across Graphcore and with open-source communities to deliver software developers can trust.\n What we’re looking for\n · Strong software engineering skills, with the judgement to manage quality, complexity and technical debt. · Experience developing in Python and C++. · Experience in compiler development. · Ability to manage complex technical tasks with cross-team dependencies. · Clear communication skills, with a proactive approach to collaboration and technical leadership. · Interest in AI, ML frameworks, performance optimisation or computationally intensive engineering.\n Benefits\n · Flexible working: Balance your work and personal life with greater flexibility · Generous leave: Take time to rest, recharge and enjoy life outside of work · Retirement planning support: Up to 5% matched pension · Phantom equity: Share in Graphcore’s success · Workplace experience: Enjoy thoughtfully designed office spaces for collaboration, with free food and an on-site barista to support your day · Peace of mind protection: Income protection and life assurance to provide financial security for you and your loved ones · Flexible benefits: Tailor your benefits package with a choice of additional options, including private medical insurance and dental cover · Optional benefits: Dental cover, health cash plan, private medical insurance, cycle to work scheme, give as you earn\n We welcome people from all backgrounds and experiences and are committed to building an inclusive environment where everyone can do their best work. We’re an equal opportunity employer and recognise that everyone brings different strengths and perspectives. If you need any adjustments during the interview process, just let us know - we’re happy to support you.\n Join the Team at Graphcore\n Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.\n As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.\n Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore brings together deep expertise to solve complex problems and deliver meaningful progress in AI compute.\n If you want to shape the frameworks developers use to push AI compute forward, we’d love to hear from you. Apply now to be part of the journey.","location":"Bristol, UK","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["pytorch","tensorflow","gpu","jax"],"apply_url":"https://job-boards.greenhouse.io/graphcore/jobs/8543115002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T16:13:17Z","expires_at":"2026-09-29T13:43:48.081188Z","created_at":"2026-08-27T13:44:32.065398Z","updated_at":"2026-08-30T13:43:48.213096Z","company_name":"Graphcore","company_slug":"graphcore","company_logo_url":"https://www.google.com/s2/favicons?domain=graphcore.ai\u0026sz=128","quality_score":60,"url":"https://aidevboard.com/job/a7e6ada4-4484-41e6-91b8-e34294561d01"},{"id":"e16de583-aee5-40a6-8e80-3e74a7a18590","company_id":"4ed3e523-b627-46ed-8dae-04c2ea823be2","title":"Campus AI/ML Researcher (Intern)","slug":"campus-aiml-researcher-afcaa3f9","description":"Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. \n Our trading teams are each comprised of a dynamic group of traders, quantitative researchers, and engineers who work together to examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models. \n ------------------------------- *Candidates should be interested in working in Asia for their full-time job after graduation. \n *INTERNATIONAL STUDENTS are encouraged to apply.  ------------------------------- \n Who Should Apply? \n \n We are seeking researchers with a demonstrated ability to apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains.  \n The ideal person for this role will be capable of implementing an open-ended research project from concept to production and continuously improving model design, tools, and infrastructure.  \n Potential projects may target any area of the quantitative research and monetization process. We believe that successful research efforts require a fluid mix of skills including ML expertise, engineering pragmatism, statistics and market intuition. \n No prior knowledge of finance or trading is necessary. We’ll give you the training that you’ll need. \n Other duties as assigned or needed. \n \n Skills You'll Need:  \n \n Creative thinkers who are driven, self-motivated, and eager to solve challenging problems with a pragmatic outlook \n Strong publication record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent and/or contributions to open-source AI research \n Familiarity with ML libraries/frameworks such as PyTorch, TensorFlow, and/or JAX \n Proficiency in Python and/or C++ \n Ability to thrive in a collaborative, team-oriented environment \n Ability to reason through quantitative problems and communicate effectively with trading researchers \n Strong general ML background with exposure to modern deep learning techniques and/or language modeling architectures (e.g. transformers, SSMs) \n Reliable and predictable availability required \n \n If you have outstanding skills in math, ML, and programming, and you are curious about the challenge of improving research with daily feedback from competitive markets, we hope you’ll apply.","location":"Hong Kong","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["pytorch","tensorflow","jax","deep-learning","research"],"apply_url":"https://www.jumptrading.com/hr/job?gh_jid=8027938","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T07:43:03Z","expires_at":"2026-09-29T13:47:30.057537Z","created_at":"2026-08-25T18:32:58.854911Z","updated_at":"2026-08-30T13:47:30.186252Z","company_name":"Jump Trading","company_slug":"jump-trading","company_logo_url":"https://www.google.com/s2/favicons?domain=jumptrading.com\u0026sz=128","quality_score":60,"url":"https://aidevboard.com/job/e16de583-aee5-40a6-8e80-3e74a7a18590"},{"id":"6a4c37ba-0f4f-4e3a-a7b7-38b40b802033","company_id":"56a8b208-4137-4ef2-988e-6fbd4d5cdc5c","title":"Senior Staff LLM Inference Engineer","slug":"principal-llm-inference-engineer-41699798","description":"At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.\n\nWe value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution.  Ready to come find your playground? Together, we can help shape the endless possibilities of AI. \n\nD-Matrix Frontier Group sits at the leading edge of what’s possible with LLM inference on heterogeneous hardware. Our charter spans the full stack: from pathfinding emerging use cases and novel deployment patterns to deep optimization of inference kernels to building proof-of-concept systems that showcase D-Matrix’s unique computational fabric. We are an applied research and engineering team that moves fast, ships real systems, and works directly with product and hardware teams to shape the roadmap.\n\nWe build the tools, runtimes, and frameworks that let frontier AI models run efficiently and cost-effectively across heterogeneous deployments combining D-Matrix silicon with CPUs, GPUs, and custom accelerators. Our work powers everything from benchmarking and evaluation pipelines to production-grade inference serving.\n\nThis Role\n\nWe are hiring end-to-end inference engineers who are comfortable going from a novel research idea to a deployed, optimized system. You will work at every layer of the inference stack from kernel-level optimization to distributed orchestration to high-level serving APIs.\n\nThis role could be a great match for you if you:\n\n - Have deep intuition for modern generative AI architectures and how to squeeze performance out of them at inference time.\n\n - Are familiar with the internals of open-source inference frameworks (vLLM, SGLang, TensorRT-LLM, etc.) and can extend or replace them when needed.\n\n - Enjoy pathfinding new use cases — exploring heterogeneous deployment topologies and building early-stage POCs that prove out new ideas.\n\n - Are results-oriented with a strong bias toward action; you own problems end-to-end from prototype to optimization to handoff.\n\n - Are energized by working at the intersection of novel hardware and frontier models and want your work to directly influence how next-generation AI silicon is used.\n\n - Value clear communication and thrive in a small, high-ownership team environment.\n\nResponsibilities\n\n - Identify and prototype emerging LLM inference use cases suited to heterogeneous hardware deployments.\n\n - Build compelling proof-of-concept systems that demonstrate D-Matrix capabilities to customers, partners, and internal stakeholders.\n\n - Develop and tune custom kernels and operator-level optimizations to maximize throughput and minimize latency.\n\n - Drive quantization, sparsity, and batching strategies tailored to the D-Matrix computational model.\n\n - Build and maintain inference runtimes, serving frameworks, and evaluation tooling.\n\n - Contribute to distributed inference systems: tensor/pipeline parallelism, disaggregated prefill/decode, KV-cache management.\n\n - Work closely with hardware architects to provide firmware and compiler teams with actionable inference workload insights.\n\n - Partner with product and business development to translate POCs into customer-facing demonstrations.\n\n - Contribute to technical publications, whitepapers, and open-source projects that advance D-Matrix visibility.\n   \n\nRequired Qualifications\n\n - Bachelor’s degree in Computer Science, Electrical Engineering, or a related field, and 10+ years of relevant engineering experience or equivalent demonstrated experience.\n\n - Master’s or PhD in Computer Science, Electrical Engineering, or a related field preferred, with 6+ years of relevant industry experience.\n\n - Strong proficiency in Python and C/C++.\n\n - Hands-on experience optimizing LLM inference — attention kernels, KV cache, batching strategies, quantization (INT8/FP8/INT4).\n\n - Experience with at least one major inference framework (vLLM, SGLang, TensorRT-LLM, ONNX Runtime, or similar) at a contributor level.\n\n - Familiarity with GPU kernel programming (CUDA/Triton) and performance profiling tools.\n\nPreferred Qualifications\n\n - Experience with heterogeneous compute deployments — scheduling inference workloads across dissimilar hardware (accelerators, CPUs, GPUs).\n\n - Familiarity with custom silicon or ASIC-based inference (beyond GPU-only environments).\n\n - Experience with distributed inference: tensor parallelism, pipeline parallelism, disaggregated serving.\n\n - Contributions to open-source inference or ML systems projects.\n\n - Experience with production inference serving at scale (latency SLOs, continuous batching, multi-model serving).\n\n - Familiarity with speculative decoding, mixture-of-experts routing, or long-c","location":"San Jose, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["gpu","llm","mlops","generative-ai","jax","inference"],"apply_url":"https://jobs.ashbyhq.com/d-matrix/f36b38d8-1d7c-40ad-9649-0c9bdef8429a/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T14:48:28.676Z","expires_at":"2026-09-29T13:40:42.4635Z","created_at":"2026-07-01T14:10:07.325211Z","updated_at":"2026-08-30T13:40:42.592749Z","company_name":"d-Matrix","company_slug":"d-matrix","company_logo_url":"https://www.google.com/s2/favicons?domain=d-matrix.ai\u0026sz=128","quality_score":60,"url":"https://aidevboard.com/job/6a4c37ba-0f4f-4e3a-a7b7-38b40b802033"},{"id":"4a8f3140-f73b-4277-aafd-f07e05223e49","company_id":"0db53cc2-2dff-417b-9e26-a5aabb77fea9","title":"Research Engineer/Scientist, Simulation","slug":"research-engineerscientist-simulation-5b4ba7e4","description":"Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model that generalizes and self-improves across varied environments with commercial-grade performance. Dyna's robots have been deployed at customers across multiple industries. Its frontier model has the top generalization and performance in the industry.\n\n\n\nDyna Robotics has raised over $140M, backed by top investors including CRV, First Round Capital, Robostrategy, Salesforce Ventures, NVentures, Amazon, Samsung Next, and LG Technology Ventures. Our team brings together engineers and researchers from Google, Meta, Apple, Amazon, Cruise, Aurora, NVIDIA, along with academic roots at Stanford, Berkeley, MIT, UPenn, and beyond. We're positioned to redefine the landscape of robotic automation.\n\n\n\n\nPOSITION OVERVIEW\n\nAs a Research Engineer/Scientist focused on Simulation, you will build the simulated worlds that power Dyna's loco-manipulation policies. Our robot is a wheeled mobile manipulator. Mobility and manipulation are tightly coupled, not two separate problems, so simulating one without the other misses what actually matters for our policies. You'll own the pipeline end-to-end: from real-to-sim reconstruction of the facility-scale environments our robots navigate, through procedural scene generation, to sim-based training data and evaluation for policies that jointly control base motion and arm manipulation.\n\n\n\n\nWHAT YOU'LL DO\n\nReal-to-Sim Reconstruction: Build a pipeline that reconstructs real, facility-scale environments into simulation (3D reconstruction, photorealistic re-rendering). That means full rooms and aisles our wheeled base navigates, not just tabletop scenes.\n\nFacility-Scale Scene Generation: Procedurally generate navigable environments (layouts, obstacles, object placement) that stress-test loco-manipulation policies across the range of spaces our robots actually operate in.\n\nLoco-Manipulation Sim-for-Data-Gen: Generate synthetic training data for policies that jointly reason about base positioning and arm manipulation (approach angles, reachability, obstacle-aware repositioning), and feed real-world failure modes back into new sim scenarios.\n\nLoco-Manipulation Evaluation: Build simulation benchmarks that test the joint base+arm policy (VLA / imitation learning / diffusion models) as a whole, not manipulation in isolation on a fixed base.\n\nRendering for ML, Not Just Physics: Push photorealistic rendering quality specifically to close the sim-to-real gap for vision-based loco-manipulation. Rendering fidelity is a first-class deliverable, not an afterthought on top of physics accuracy.\n\nCross-Team Collaboration: Partner with the AI Research team on where simulated data and evaluation most accelerate loco-manipulation policy development, and with Data Ops on what's worth capturing in the real world vs. generating in sim.\n\n\n\n\nWHAT YOU'LL BRING\n\n - MS or PhD in CS/Robotics/Graphics, or equivalent hands-on experience. We care more about demonstrated depth building simulation/rendering systems than a fixed years-of-experience bar.\n\n - Hands-on experience with simulation stacks (MuJoCo, Isaac Sim/Isaac Lab, SAPIEN, Omniverse, Blender, or similar) for robotics or graphics.\n\n - Experience with procedural scene/asset generation, domain randomization, or photorealistic rendering pipelines at scale (not academic-scale one-off scenes).\n\n - Familiarity with training/evaluating manipulation or loco-manipulation policies (imitation learning, VLA, diffusion, or RL) and how simulated data actually feeds into them.\n\n - Experience simulating mobile bases (wheeled, tracked, or holonomic) is a real plus. Most of the field's simulation talent comes from fixed-arm tabletop manipulation or legged-humanoid balance/gait work, and neither maps cleanly onto a wheeled mobile manipulator.\n\n - Prior research or engineering background in loco-manipulation or whole-body control itself, coordinating mobile-base motion with arm manipulation, independent of any simulation-specific experience.\n\n - Strong Python skills; comfort with PyTorch or JAX for anything touching model training/eval.\n\n - Senior enough to define your own research agenda and exercise independent judgment on where simulation adds the most leverage. This is closer to a founding-type ownership role than a narrow IC slot on an existing pipeline.\n\n\nBONUS POINTS FOR\n\n - Experience with real-to-sim-to-real pipelines (3D reconstruction, NeRF/Gaussian splatting, differentiable rendering).\n\n - Exposure to world-model / video-prediction research (e.g. learned dynamics or latent world models) as a complement to classical physics simulation.\n\n - GPU-scale physics simulation experience (CUDA, large-batch parallel sim) rather than single-instance sim.\n\n - Background simulating wheeled/mobile manipulators specifically (e.g. Boston Dynamics Stretch, warehouse/logistics robotics) rather than legged humanoids or fixed-base arms.\n\n - Publications at CoRL, RSS, ICRA, NeurIPS, CVPR, or SIGGR","location":"Redwood City, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["search","generative-ai","computer-graphics","robotics","pytorch","diffusion-models","jax","gpu"],"apply_url":"https://jobs.ashbyhq.com/dyna-robotics/942cd530-b2b7-4141-8aaa-d844175e26ac/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-04T17:48:16.154Z","expires_at":"2026-09-29T13:40:25.205149Z","created_at":"2026-08-25T18:29:34.456126Z","updated_at":"2026-08-30T13:40:25.342509Z","company_name":"Dyna Robotics","company_slug":"dyna-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=dyna.co\u0026sz=128","quality_score":60,"url":"https://aidevboard.com/job/4a8f3140-f73b-4277-aafd-f07e05223e49"}],"page":1,"per_page":20,"total":36,"total_is_exact":true,"total_pages":2}