{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL (Reinforcement Learning) ","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","code-generation","search","pytorch","llm","jax","fine-tuning","gpu"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-08-15T14:00:29.666185Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-07-16T14:00:29.796553Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"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","gpu","generative-ai","llm","jax","computer-graphics"],"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":"2026-08-15T14:05:36.795Z","created_at":"2026-07-15T14:06:51.909822Z","updated_at":"2026-07-16T14:05:36.92287Z","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":"2480a8c2-7a7a-4977-ae22-9777888026bf","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Foundation Model Data, Software Engineer","slug":"foundation-model-data-software-engineer-5d5869a2","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 mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. \n In this hybrid role, you will report to a Principal Research Scientist. \n You will: \n \n Build and operate the petabyte-scale data systems and ML pipelines at the heart of Waymo's foundation model development\n Shepherd cutting-edge foundation models from research prototypes to robust components within the Waymo Driver\n Develop automated, high-performance infrastructure to ensure the dependable and efficient rollout of data solutions at the forefront of Waymo foundation model program\n Wield large-scale compute and frameworks like Flume/Beam and JAX to process massive datasets for training, evaluating and deploying complex models\n Drive significant leaps in the speed, reliability, and efficiency of the end-to-end ML development lifecycle\n Partner with experts in AI Foundations, ML, and Platform to transform model innovations into tangible improvements to Waymo’s product\n \n You have: \n \n Undergraduate degree in Computer Science, Machine Learning, Robotics, similar technical field of study, or equivalent practical experience\n Proficiency in Python and C++\n Experience building or maintaining large-scale data pipelines or ML infrastructure (e.g., Flume, Spark, Borg, Kubeflow)\n Strong hands-on SWE skills, able to drive development of large, complex shared codebases\n \n We prefer: \n \n Experience designing and building distributed systems or MLOps platforms (e.g., model versioning, experiment tracking, CI/CD for ML)\n Familiarity with one of the modern deep learning frameworks (e.g. Pytorch, JAX, Tensorflow)\n Experience in AV planning and related research\n Prior work in an industrial or research setting developing methodologies for data-centric improvement of ML models\n Previous data curation experience and multimodal experience\n \n  \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include: \n \n Health, dental, vision, life, disability insurance \n Retirement Benefits: 401(k) with company match \n Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment \n Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) \n Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks \n Baby Bonding Leave: 18 weeks \n Holidays: 13 paid days per year \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","data-pipeline","deep-learning","jax","autonomous-vehicles","pytorch","robotics","tensorflow"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8031883","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-29T18:49:55Z","expires_at":"2026-08-15T14:05:05.96732Z","created_at":"2026-06-30T14:04:16.964547Z","updated_at":"2026-07-16T14:05:06.083773Z","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/2480a8c2-7a7a-4977-ae22-9777888026bf"},{"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":["jax","llm","reinforcement-learning","pre-training","generative-ai","agents","pytorch","data-pipeline"],"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":"2026-08-15T14:03:44.146834Z","created_at":"2026-04-17T19:30:23.022034Z","updated_at":"2026-07-16T14:03:44.265114Z","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":"63cf43b2-24aa-49be-a1a0-8e7071a975af","company_id":"e452e377-b504-47ba-85cc-b47aa09c3067","title":"AI/ML Scientist, Protein Foundation Models","slug":"aiml-scientist-protein-foundation-models-ccdf307f","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 Position \n Manifold's AI team is actively training protein foundation models on our proprietary experimental datasets. Our generative antibody design model, mBER, has already demonstrated controllable de novo binder design across multiple million-scale screening campaigns, and the team is now scaling foundation model capabilities to push well beyond current performance. We are looking for an AI/ML Scientist to join this effort. You will work alongside our existing model training team to accelerate the development of foundation models fine-tuned on Manifold's data, bringing additional depth in pre-training methodology, architecture development, and large-scale training. Your work will directly improve mBER's design capabilities and unlock new modeling paradigms for the broader team. You'll own foundation model projects end-to-end, from architecture selection and training infrastructure to evaluation against real experimental outcomes, while contributing to the team's shared research agenda.\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 Advance the team's ongoing foundation model training efforts—pretraining, fine-tuning, and evaluating folding, docking, language, and generative design models on Manifold's proprietary experimental data\n Bring depth in training methodology, architecture selection, and optimization to complement the existing team's expertise\n Develop and scale training pipelines for distributed, multi-GPU and multi-node training runs\n Integrate foundation model outputs into mBER to improve binder design success rates and enable new design capabilities\n Design and execute ML experiments with clear hypotheses, rigorous evaluation frameworks, and systematic analysis\n Establish best practices for mixed-precision training, gradient checkpointing, and computational efficiency at scale\n Produce clear documentation and analysis supporting architecture and training decisions\n \n Required Qualifications \n \n Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding, docking, language models, or generative design) with published or otherwise demonstrable results\n Strong familiarity with AlphaFold architecture and training methodology\n 2+ years of hands-on experience with PyTorch and/or JAX for deep learning\n Experience with large-scale model training: distributed training, multi-GPU/multi-node setups, mixed precision, gradient checkpointing\n Solid understanding of deep learning architectures (transformers, attention mechanisms, diffusion/flow matching) and optimization techniques\n Experience working with protein structure data (PDB, mmCIF) and/or protein sequence datasets\n Strong statistical analysis and experimental design skills\n Proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)\n Self-directed researcher who can balance guidance with independence\n Excellent written and verbal communication skills for cross-functional collaboration\n \n Preferred Qualifications \n \n Experience with protein generative design methods (e.g., RFdiffusion, ProteinMPNN, flow matching approaches)\n Experience with protein language models (e.g., ESM family)\n Published research in computational biology, protein design, or structural biology\n Experience training on proprietary or domain-specific biological datasets\n Familiarity with Ray for distributed computing\n Experience with Kubernetes (EKS) and cloud computing platforms (AWS)\n Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques\n Experience working with agentic AI coding tools for fast, parallelized execution of modeling experiments\n Previous biotech/pharma industry experience\n \n This Role Might Be Perfect For You If: \n \n You have deep experience training protein foundation models and want to apply that expertise to some of the richest proprietary experimental datasets in the field\n You're excited about pushing beyond public model performance by leveraging unique, large-scale in vivo screening data\n You thrive in high-ownership roles where you can drive research direction while collaborating with a tight-kni","salary_min":140000,"salary_max":225000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["jax","fine-tuning","distributed-systems","agents","deep-learning","generative-ai","pytorch","pre-training"],"apply_url":"https://job-boards.greenhouse.io/manifoldbio/jobs/5106955007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-13T21:35:41Z","expires_at":"2026-08-15T14:15:35.335848Z","created_at":"2026-04-16T18:53:13.456204Z","updated_at":"2026-07-16T14:15:35.484232Z","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/63cf43b2-24aa-49be-a1a0-8e7071a975af"},{"id":"3ac98716-e6fe-4479-ba32-b0a58040f8fa","company_id":"e452e377-b504-47ba-85cc-b47aa09c3067","title":"AI/ML Scientist","slug":"aiml-scientist-7a7fb43c","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 Position \n Manifold Bio is seeking an exceptional Machine Learning Scientist to lead research initiatives within our AI team and drive innovation in protein design methodologies. This role will support and enrich the team’s current generative de novo protein design capabilities, as well as break ground on building foundational models on relevant protein therapeutic properties including binding affinity, developability, and in vivo biodistribution and pharmacokinetic (PK) properties. You will be expected to independently conceive, design and execute high-impact research projects that push the boundaries of AI applications in protein design, while contributing to publications and representing Manifold’s scientific contributions to the broader research community.\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 Lead independent research projects exploring novel AI/ML approaches for protein design and optimization\n Investigate and integrate novel data types to enhance our binder design capabilities and therapeutic development\n Develop and validate cutting-edge machine learning methodologies for molecular engineering applications\n Mentor and guide junior scientists and engineers in advanced ML techniques and research methodologies\n Drive strategic research planning and identify promising new directions for AI-driven drug discovery\n Lead cross-functional teams to translate research breakthroughs into therapeutic applications\n Provide scientific expertise for business development opportunities and strategic partnerships\n \n Required Qualifications \n \n PhD in Machine Learning, Computational Biology, Bioinformatics, Computer Science, or related field\n 5+ years of industry experience in machine learning research, preferably in biotechnology or pharmaceutical settings\n Expert-level proficiency in PyTorch and/or JAX with extensive experience in novel architecture development\n Deep expertise in protein structure analysis, molecular dynamics, computational biology, or structural bioinformatics\n Extensive knowledge of modern AI advances in protein analysis, design, and optimization\n Advanced understanding of deep learning architectures, particularly transformers for molecular applications, as well as generative modeling approaches (autoregressive, diffusion, flow-matching, etc.)\n Proven experience leading research teams and mentoring junior scientists\n Strong background in statistical analysis, experimental design, and rigorous scientific methodology\n Experience with distributed computing, high-performance computing, and large-scale ML system design\n Outstanding written and verbal communication skills with experience presenting to scientific and business audiences\n Demonstrated ability to translate research innovations into practical applications\n \n Preferred Qualifications \n \n Previous experience in biotech/pharma industry with focus on AI-driven drug discovery\n Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques\n Experience with large-scale data engineering, cloud platforms, and production ML systems\n Demonstrated track record of published research in top-tier ML, computational biology, or protein design journals\n Track record of successful grant funding and research proposal writing\n Experience with regulatory considerations for AI applications in therapeutic development\n Strong network within the computational biology and protein design research communities\n Demonstrated leadership in cross-functional scientific teams\n Experience with intellectual property development and patent applications\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 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","salary_min":140000,"salary_max":225000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["jax","pytorch","deep-learning","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/manifoldbio/jobs/5106949007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-13T21:34:06Z","expires_at":"2026-08-15T14:15:35.24091Z","created_at":"2026-04-16T18:53:13.380288Z","updated_at":"2026-07-16T14:15:35.373005Z","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/3ac98716-e6fe-4479-ba32-b0a58040f8fa"},{"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":["jax","mlops","search","data-pipeline","gpu","llm","deep-learning","pytorch"],"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":"2026-08-15T14:15:35.139381Z","created_at":"2026-04-16T18:53:13.262577Z","updated_at":"2026-07-16T14:15:35.284248Z","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":"389814ab-a372-46f9-bf10-8b65a79853cf","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Principal Software Engineer, ML System Architect","slug":"principal-software-engineer-ml-system-architect-fb403779","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Waymo’s Systems Intelligence and ML team works with Research and Production teams to develop and deploy models that are core to our autonomous driving software. Waymo's AI is at the heart of this mission, and we are increasingly leveraging large-scale Foundation Models to unlock new capabilities for the Waymo Driver. Join Waymo to architect and build a unified, large-scale AI platform leveraging Google DeepMind's latest foundation models (like Gemini) for comprehensive world understanding and generation, to accelerate the development and distillation of models powering the world's most experienced driver.\n In this hybrid role, you will report to our Director of Engineering who leads Systems Intelligence and Machine Learning. We are seeking a deeply experienced Principal Software Engineer to provide the overarching technical vision, architectural design, and cross-team leadership to make Waymo’s foundation model systems nextgen a success. This role is pivotal in transforming Waymo's offboard ML landscape from a fragmented set of models and tools into a cohesive, efficient, and powerful platform centered around a unified foundation model recipe, deeply integrated with Google Deepmind's latest innovations with Gemini. You will be the technical authority defining how Waymo builds, trains, and utilizes these large models offboard to ultimately accelerate onboard deployment and improvements.\n You will: \n \n Architect ML Systems: Define and drive the technical roadmap for the platform, encompassing codebase unification, data pipelines, model architecture, training recipes, and evaluation frameworks.\n Codebase Consolidation \u0026 Best Practices: Lead the unification of existing forked locations of foundation model component codebases into a production-hardened, shared repository. Establish and enforce rigorous coding standards, testing practices, and API designs to ensure long-term codebase health and developer velocity.\n Google Deepmind Integration \u0026 API Definition: Serve as the primary technical interface between Waymo's offboard model development and Google Deepmind's core model and framework teams. Define clear APIs and integration patterns, ensuring Waymo can seamlessly leverage and contribute to Google Deepmind's advancements while maintaining stability and control.\n Unify Core Components: Drive the consolidation of tokenization/de-tokenization strategies, data formats, input pipelines, and evaluation methodologies across all offboard Foundation Model use cases.\n Scalable Training \u0026 Distillation: Architect for efficient large-scale distributed training (large scale) and establish a common, efficient distillation setup to transfer knowledge from large teacher models to onboard student models.\n Technical Leadership \u0026 Influence: Provide technical mentorship, guidance, and direction to engineers across multiple teams within SIML and AI Foundations. Drive alignment on technical decisions with senior stakeholders across Waymo and Google Deepmind.\n Drive Efficiency: Instill a culture of efficiency in model development, training, and resource utilization, aiming for high ML Productivity.\n \n You have: \n \n Master's degree or PhD in Computer Science or a related field.\n 12+ years of experience in software engineering, with at least 8+ years focused on large-scale machine learning systems, deep learning frameworks, and AI infrastructure.\n A track record of architecting and delivering complex, high-impact ML platforms or models.\n Deep expertise in Python, C++, and ML frameworks like JAX and TensorFlow.\n Extensive experience with large-scale distributed training on TPUs/GPUs and associated challenges.\n Demonstrated ability to design robust, scalable, and maintainable software architectures and APIs.\n Understanding of data pipelines, storage systems, and tokenization techniques.\n Experience working effectively with research and product teams, and influencing across organizational boundaries.\n Technical leadership skills, with the ability to drive strategy, influence across teams, and mentor other engineers.\n Communication skills, with the ability to articulate complex technical vision and drive alignment, capable of conveying complex technical ideas clearly.\n \n We prefer: \n \n Experience with multimodal and generative models.\n Ex","salary_min":349000,"salary_max":431000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["distributed-systems","generative-ai","autonomous-vehicles","data-pipeline","api-design","robotics","tensorflow","jax"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7773177","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-01T22:20:30Z","expires_at":"2026-08-15T14:05:07.493828Z","created_at":"2026-04-13T09:40:16.24306Z","updated_at":"2026-07-16T14:05:07.609927Z","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/389814ab-a372-46f9-bf10-8b65a79853cf"},{"id":"41e20e83-f1c0-412c-b84c-10a516f9fc81","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior/Staff Deep Reinforcement Learning Engineer","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":["cloud","agents","deep-learning","distributed-systems","jax","fine-tuning","autonomous-vehicles","reinforcement-learning"],"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":"2026-08-15T14:20:04.411094Z","created_at":"2026-04-17T04:55:26.063966Z","updated_at":"2026-07-16T14:20:04.610815Z","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","reinforcement-learning","jax","pytorch","deep-learning","research","machine-learning"],"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":"2026-08-15T14:18:42.624567Z","created_at":"2026-04-17T00:25:48.704181Z","updated_at":"2026-07-16T14:18:42.816178Z","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":"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":["jax","pytorch","deep-learning","cloud","payments","robotics","computer-vision","tensorflow"],"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":"2026-08-15T14:07:45.050202Z","created_at":"2026-04-13T09:43:05.683344Z","updated_at":"2026-07-16T14:07:45.211431Z","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":"2026-08-15T14:18:51.416334Z","created_at":"2026-04-17T00:48:46.067742Z","updated_at":"2026-07-16T14:18:51.54231Z","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":"Research 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":["nlp","deep-learning","pytorch","autonomous-vehicles","robotics","generative-ai","jax","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":"2026-08-15T14:05:07.682787Z","created_at":"2026-04-13T09:40:16.579726Z","updated_at":"2026-07-16T14:05:07.801851Z","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":["llm","pytorch","alignment","tensorflow","reinforcement-learning","cloud","agents","jax"],"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":"2026-08-15T14:19:51.138048Z","created_at":"2026-04-17T04:55:11.806188Z","updated_at":"2026-07-16T14:19:51.263691Z","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":["data-pipeline","reinforcement-learning","mlops","jax","fine-tuning","pytorch","distributed-systems","tensorflow"],"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":"2026-08-15T14:19:50.909019Z","created_at":"2026-04-17T04:55:11.631102Z","updated_at":"2026-07-16T14:19:51.032237Z","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":["jax","llm","alignment","gpu","distributed-systems","pytorch","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":"2026-08-15T14:00:25.199651Z","created_at":"2026-04-13T09:35:57.775908Z","updated_at":"2026-07-16T14:00:25.332066Z","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":"Member of Technical Staff - 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":["pytorch","deep-learning","gpu","jax","data-pipeline"],"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":"2026-08-15T14:03:44.546211Z","created_at":"2026-04-13T09:38:43.081285Z","updated_at":"2026-07-16T14:03:44.671357Z","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":"c6d1c92c-abd1-491a-a986-17d58b86795b","company_id":"a0b04b48-9259-414d-93bd-ae677520bef1","title":"PyTorch Engineer","slug":"pytorch-engineer-e7cf7aed","description":"Salary Range: 260,400 - 352,200 \n Subject to alignment to the responsibilities and duties of the role \n About Graphcore  \n Graphcore is one of the world’s leading innovators in Artificial Intelligence compute.  \n 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 enjoys a culture of continuous learning and constant innovation.  \n Job Summary     \n Reporting to a Team Lead in the frameworks team you will play a pivotal role in designing, implementing, optimising, maintaining and supporting the software required to ensure the machine learning accelerators that Graphcore develop, enjoy first-class support in state-of-the-art machine learning frameworks such as PyTorch and Triton.    \n This role sees you joining our PyTorch team, where you will be part of a SCRUM team working on delivering new features, optimise performance, reviewing code changes, writing technical documentation, working with upstream communities, maintaining the code base and supporting users.    \n In this role you will closely collaborate with other engineers, both within the PyTorch team as well as other engineering teams. You help the team coordinate and deliver complex, open-ended technical tasks. You are pro-active and an excellent communicator. You will develop deep expertise in the PyTorch project and will (in time) contribute to the team’s technical direction and processes. You understand the importance of managing code quality and code complexity and balancing this against the need to deliver business outcomes.   \n Note that the job title and associated benefits will be tailored to the successful candidate’s level of experience at the job offer stage to one of Software Engineer, Staff Engineer, Senior Staff Engineer, Principal Software Engineer or Senior Principal Software Engineer.    \n Note that as the machine learning software landscape is quite fast moving and as such flexibility is essential – over time you may be asked to work on frameworks other than PyTorch.   \n The Team   \n The Frameworks team ensures Graphcore hardware works seamlessly with the tools that ML engineers and researchers love – Pytorch, Triton, Jax and TensorFlow. We’re a talented and diverse team of engineers and we foster a culture of collaboration, openness and learning. All our software teams follow agile working practices, and we care deeply about both ease-of-use as well as performance. We work closely work with other Graphcore teams as well as leading open-source communities.   \n By joining us, you’ll join our exciting journey on the cutting edge of the machine learning industry. Your contributions will make a real difference – enabling machine learning engineers and researchers to unlock the full potential of Graphcore’s hardware.    \n Responsibilities and Duties   \n \n Designing and implementing new features \u0026 maintaining and supporting existing features. \n Developing and maintaining unit tests, component tests and integration tests. \n Optimising the software stack to make it more performant for our users. \n Managing complex technical tasks with cross-team dependencies. \n Managing code quality, code complexity and technical debt. \n Provide technical leadership, contributing the team’s technical direction and processes. \n Contributing to documentation, including user manuals and tutorials. \n Conducting code reviews. \n Resolving regressions, performance issues and software defects. \n Coaching and mentoring other team members. \n Contributing to a collaborative team culture. \n Contribute to continuous improvements to improve ways of working. \n Working with upstream open-source development teams. \n \n Candidate Profile  \n Essential:   \n \n Demonstrable strong software engineering skills. \n Experience of Python development. \n Experience of developing performant C++ applications in a commercial setting. \n At least one of: \n Experience of writing ML kernels. \n Experience of compiler development. \n Experience of using and/or development of ML Frameworks. \n Experience in a computationally intensive engineering field. \n \n Desirable:   \n \n Demonstrable knowledge of AI/ML. \n Understanding of computing architectures. \n Experience of maintaining and supporting a complex code base. \n Experience in profiling / optimising high performance code. \n \n Benefits \n In addition ","location":"Gdańsk, Pomeranian Voivodeship, Poland","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["tensorflow","gpu","pytorch","jax"],"apply_url":"https://job-boards.greenhouse.io/graphcore/jobs/8634341002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T11:09:08Z","expires_at":"2026-08-15T14:14:18.613886Z","created_at":"2026-07-15T14:15:40.354643Z","updated_at":"2026-07-16T14:14:18.738579Z","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/c6d1c92c-abd1-491a-a986-17d58b86795b"},{"id":"2e0df513-7939-4a20-a6e5-6f4da296515a","company_id":"a0b04b48-9259-414d-93bd-ae677520bef1","title":"PyTorch Engineer","slug":"pytorch-engineer-b1a72095","description":"About Graphcore  \n Graphcore is one of the world’s leading innovators in Artificial Intelligence compute.  \n 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 enjoys a culture of continuous learning and constant innovation.  \n Job Summary     \n Reporting to a Team Lead in the frameworks team you will play a pivotal role in designing, implementing, optimising, maintaining and supporting the software required to ensure the machine learning accelerators that Graphcore develop, enjoy first-class support in state-of-the-art machine learning frameworks such as PyTorch and Triton.    \n This role sees you joining our PyTorch team, where you will be part of a SCRUM team working on delivering new features, optimise performance, reviewing code changes, writing technical documentation, working with upstream communities, maintaining the code base and supporting users.    \n In this role you will closely collaborate with other engineers, both within the PyTorch team as well as other engineering teams. You help the team coordinate and deliver complex, open-ended technical tasks. You are pro-active and an excellent communicator. You will develop deep expertise in the PyTorch project and will (in time) contribute to the team’s technical direction and processes. You understand the importance of managing code quality and code complexity and balancing this against the need to deliver business outcomes.   \n Note that the job title and associated benefits will be tailored to the successful candidate’s level of experience at the job offer stage to one of Software Engineer, Staff Engineer, Senior Staff Engineer, Principal Software Engineer or Senior Principal Software Engineer.    \n Note that as the machine learning software landscape is quite fast moving and as such flexibility is essential – over time you may be asked to work on frameworks other than PyTorch.   \n The Team   \n The Frameworks team ensures Graphcore hardware works seamlessly with the tools that ML engineers and researchers love – Pytorch, Triton, Jax and TensorFlow. We’re a talented and diverse team of engineers and we foster a culture of collaboration, openness and learning. All our software teams follow agile working practices, and we care deeply about both ease-of-use as well as performance. We work closely work with other Graphcore teams as well as leading open-source communities.   \n By joining us, you’ll join our exciting journey on the cutting edge of the machine learning industry. Your contributions will make a real difference – enabling machine learning engineers and researchers to unlock the full potential of Graphcore’s hardware.    \n Responsibilities and Duties   \n \n Designing and implementing new features \u0026 maintaining and supporting existing features. \n Developing and maintaining unit tests, component tests and integration tests. \n Optimising the software stack to make it more performant for our users. \n Managing complex technical tasks with cross-team dependencies. \n Managing code quality, code complexity and technical debt. \n Provide technical leadership, contributing the team’s technical direction and processes. \n Contributing to documentation, including user manuals and tutorials. \n Conducting code reviews. \n Resolving regressions, performance issues and software defects. \n Coaching and mentoring other team members. \n Contributing to a collaborative team culture. \n Contribute to continuous improvements to improve ways of working. \n Working with upstream open-source development teams. \n \n Candidate Profile  \n Essential:   \n \n Demonstrable strong software engineering skills. \n Experience of Python development. \n Experience of developing performant C++ applications in a commercial setting. \n \n At least one of:\n \n Experience of writing ML kernels. \n Experience of compiler development. \n Experience of using and/or development of ML Frameworks. \n Experience in a computationally intensive engineering field. \n \n Desirable:   \n \n Demonstrable knowledge of AI/ML. \n Understanding of computing architectures. \n Experience of maintaining and supporting a complex code base. \n Experience in profiling / optimising high performance code. \n \n Benefits \n In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private me","location":"Bristol, UK","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","jax","tensorflow","gpu"],"apply_url":"https://job-boards.greenhouse.io/graphcore/jobs/8630718002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T11:09:06Z","expires_at":"2026-08-15T14:14:18.524802Z","created_at":"2026-07-15T14:15:40.454147Z","updated_at":"2026-07-16T14:14:18.660211Z","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/2e0df513-7939-4a20-a6e5-6f4da296515a"},{"id":"baa3b244-78cc-4c74-8e83-df0c8ccae2bf","company_id":"a0b04b48-9259-414d-93bd-ae677520bef1","title":"2026 Graduate Software Engineer - Triton","slug":"2026-graduate-software-engineer-triton-893e3852","description":"About the job \n Build the software that helps machine learning developers get more from Graphcore hardware. As a Graduate Software Engineer in our Triton team, you will help connect Graphcore accelerators with the ML frameworks developers use every day.\n Your work will make our software easier to use, more reliable and more performant for real AI workloads. You will learn how compilers, frameworks and hardware interact in a production engineering setting. \n You will contribute to features, tests, documentation, code reviews and performance improvements across a complex codebase. You will be supported by experienced engineers while taking ownership of meaningful work early.\n This role suits a curious graduate who wants to grow deep technical skills and see their code make a practical difference.\n  \n The Team \u0026 Culture \n The Frameworks team makes Graphcore hardware work smoothly with tools such as PyTorch, Triton, JAX and TensorFlow. The team sits close to compilers, runtime software and hardware, so you will learn from different technical perspectives.\n Work is organised through SCRUM, with clear priorities, regular feedback and shared accountability. You will own well-scoped tasks, ask questions early and build confidence through reviews, testing and documentation.\n Decisions are shaped by technical evidence, user impact and open discussion. Engineers are expected to speak up, challenge assumptions and improve how the team works.\n  \n What we’re looking for \n \n Bachelor’s or master’s degree in computer science, Maths, Machine Learning, Data Science, or a related field.\n Hands-on experience with Python or C++, gained through study, projects, placements, internships or open source.\n Practical experience or strong academic understanding of compiler development Interest in AI, machine learning frameworks, high performance software or computing architectures.\n Ability to write clear, maintainable code, tests and documentation\n Curiosity, ownership and willingness to learn through feedback, code reviews and technical discussion.\n \n   \n Benefits  \n \n Flexible working: Balance your work and personal life with greater flexibility \n Generous leave: Take time to rest, recharge and enjoy life outside of work \n Retirement planning support:  Up to 5% matched pension \n Phantom equity: Share in Graphcore’s success  \n Workplace experience: Enjoy thoughtfully designed office spaces for collaboration, with free food and an on-site barista to support your day \n Peace of mind protection: Income protection and life assurance to provide financial security for you and your loved ones \n Flexible benefits: Tailor your benefits package with a choice of additional options, including private medical insurance and dental cover \n Optional benefits:  Dental cover, health cash plan, private medical insurance, cycle to work scheme, give as you earn\n \n  \n Sponsorship  \n Applicants must have the legal right to work in the UK. Unfortunately, we are unable to provide visa sponsorship or support visa applications for this role. \n  \n Inclusion statement \n We welcome people from all backgrounds and experiences and are committed to building an inclusive environment where everyone can do their best work.\n 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  \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 Join Graphcore’s Graduate Triton Software Engineering Programme and start building the skills, confidence, and experience to turn your learning into real impact. Apply now.","location":"Bristol, UK","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["gpu","pytorch","tensorflow","jax"],"apply_url":"https://job-boards.greenhouse.io/graphcore/jobs/8615504002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-01T12:21:30Z","expires_at":"2026-08-15T14:14:12.512003Z","created_at":"2026-07-01T14:13:10.486284Z","updated_at":"2026-07-16T14:14:12.663997Z","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/baa3b244-78cc-4c74-8e83-df0c8ccae2bf"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":47,"total_pages":3}