{"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","gpu","code-generation","distributed-systems","search","jax","fine-tuning","llm"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-06-29T14:00:21.52187Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-05-30T14:00:21.633054Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"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":"ABOUT xAI \n xAI’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 xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n xAI is an equal opportunity employer. For details on data processing, view our  Recr","salary_min":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","jax","reinforcement-learning","distributed-systems","fine-tuning","pytorch","generative-ai","llm"],"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":"2026-06-29T14:02:58.244148Z","created_at":"2026-04-17T19:30:23.022034Z","updated_at":"2026-05-30T14:02:58.352155Z","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","job_type":"full-time","experience_level":"junior","tags":["generative-ai","fine-tuning","pytorch","distributed-systems","jax","pre-training","agents","deep-learning"],"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":"2026-06-29T14:14:11.250881Z","created_at":"2026-04-16T18:53:13.456204Z","updated_at":"2026-05-30T14:14:11.371328Z","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","job_type":"full-time","experience_level":"senior","tags":["pytorch","jax","distributed-systems","deep-learning"],"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":"2026-06-29T14:14:11.165982Z","created_at":"2026-04-16T18:53:13.380288Z","updated_at":"2026-05-30T14:14:11.284135Z","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","job_type":"full-time","experience_level":"junior","tags":["deep-learning","pytorch","distributed-systems","gpu","llm","data-pipeline","jax","search"],"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":"2026-06-29T14:14:11.086935Z","created_at":"2026-04-16T18:53:13.262577Z","updated_at":"2026-05-30T14:14:11.199028Z","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":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["generative-ai","jax","deep-learning","robotics","tensorflow","api-design","distributed-systems","data-pipeline"],"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":"2026-06-29T14:04:25.808727Z","created_at":"2026-04-13T09:40:16.24306Z","updated_at":"2026-05-30T14:04:25.925371Z","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":"76e00737-0f7e-4666-9a47-13aa2130117d","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer, Multimodal Perception","slug":"senior-machine-learning-engineer-perception-8b1676d1","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 Semantics team is a specialized subgroup within the Perception organization at Waymo. Our mission is to bring the immense reasoning power and innate world knowledge of massive foundation models directly onto the Waymo Driver. We focus on building an onboard multi-task, multimodal perception model designed to tackle highly complex and unpredictable \"long-tail\" scenarios.\n You Will: \n \n Architect and train large-scale, onboard ML perception models that are instrumental to ensuring vehicle safety and regulatory compliance.\n Drive cross-functional collaboration to engineer robust, high-reliability training pipelines within a dynamic, rapid-delivery environment.\n Leverage deep computer vision expertise to design novel, custom architectures from first principles to solve complex perception challenges.\n Contribute to a vibrant and positive team culture where diverse skill sets and backgrounds are valued. Support the growth of junior engineers and foster a high-performing, collaborative team environment.\n \n You Have: \n \n BS or MS in Computer Vision, Machine Learning, Robotics, or a related field.\n 4+ years of applied industry experience in autonomous vehicles, robotics, or complex ML systems.\n Fluency in Python or C++, with deep hands-on expertise in PyTorch or Jax for matrix manipulation and module implementation.\n Deep understanding and proven practical experience with model distillation frameworks and quantization techniques for real-time compute constraints.\n Demonstrated hands-on experience building, training, or deploying Multimodal Foundation Models or Vision-Language Models (VLMs).\n \n We Prefer: \n \n PhD in Computer Vision, Machine Learning, Robotics, or a related field.\n Hands-on experience managing and optimizing large-scale teacher-student training loops.\n A proven track record of successfully deploying Vision-Language queries in highly constrained, real-time environments.\n Experience with large-scale distributed training, Parameter-Efficient Fine-Tuning (PEFT), or Reinforcement Learning from Human Feedback (RLHF) for Foundation Models and VLMs.\n Deep expertise in long-context temporal reasoning for sequential decision-making or complex video understanding.\n First-author publications in premier computer vision and machine learning conferences, such as CVPR, NeurIPS, ICCV, or ECCV.\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","job_type":"full-time","experience_level":"senior","tags":["jax","autonomous-vehicles","reinforcement-learning","robotics","pytorch","distributed-systems","fine-tuning","generative-ai"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7767649","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-31T17:48:53Z","expires_at":"2026-06-29T14:04:26.576709Z","created_at":"2026-04-13T09:40:17.04301Z","updated_at":"2026-05-30T14:04:26.689611Z","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/76e00737-0f7e-4666-9a47-13aa2130117d"},{"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 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 this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 h","salary_min":168000,"salary_max":247000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","distributed-systems","robotics","agents","reinforcement-learning","cloud","healthcare","jax"],"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":"2026-06-29T14:18:34.333202Z","created_at":"2026-04-17T04:55:26.063966Z","updated_at":"2026-05-30T14:18:34.445998Z","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","job_type":"full-time","experience_level":"junior","tags":["jax","tensorflow","pytorch","deep-learning","reinforcement-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":"2026-06-29T14:17:13.441694Z","created_at":"2026-04-17T00:25:48.704181Z","updated_at":"2026-05-30T14:17:13.55678Z","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":"afc4ab04-8227-41ec-a911-440020a24163","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Research Scientist, Foundation Model (LLM/VLM)","slug":"senior-research-scientist-foundation-model-llmvlm-c80cd610","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 Applied Research team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of the 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.\n In this hybrid role you will report to a Technical Lead Manager.         \n You will:  \n \n \n Conduct applied foundation model research and development\n \n Design compelling experiments by training and evaluating large deep learning models\n \n Present results to peers and leadership\n \n Write high quality code, unit tests, and documentation\n \n You have: \n \n \n Masters or PhD in deep learning in behavior and agent modeling or simulation\n Proficiency in Python\n \n 3+ years of experience with modern deep learning frameworks. Prefer JAX or TensorFlow 2; however, Pytorch is acceptable.\n Experience in generative modeling and LLMs or VLMs (e.g., Gemini, Llama, GPT)\n \n We prefer: \n Experience with:\n \n Post-training, incl. reinforcement learning\n Distillation\n Transformer models\n Autoencoders and embeddings\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 $204,000 — $259,000 USD","salary_min":204000,"salary_max":259000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["generative-ai","jax","deep-learning","reinforcement-learning","tensorflow","autonomous-vehicles","llm","pytorch"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7572081","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-01-28T18:15:37Z","expires_at":"2026-06-29T14:04:27.779514Z","created_at":"2026-04-13T09:40:17.99515Z","updated_at":"2026-05-30T14:04:27.887893Z","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/afc4ab04-8227-41ec-a911-440020a24163"},{"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","job_type":"full-time","experience_level":"senior","tags":["cloud","robotics","pytorch","jax","computer-vision","deep-learning","payments","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":"2026-06-29T14:06:48.837727Z","created_at":"2026-04-13T09:43:05.683344Z","updated_at":"2026-05-30T14:06:48.960861Z","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","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":"2026-06-29T14:17:22.029595Z","created_at":"2026-04-17T00:48:46.067742Z","updated_at":"2026-05-30T14:17:22.151771Z","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":"efb7afd1-652b-44b3-b97c-2271f7ada531","company_id":"10df5a0b-95c8-4a89-a3a6-af92f87992f5","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-c45fd59a","description":"About this opportunity: \n At Freenome, we are seeking a Senior Machine Learning Research Engineer to join the Machine Learning Science (MLS) team, within the Computational Science department. The ideal candidate has a strong knowledge in designing and building deep learning (DL) pipelines, and expertise in creating reliable, scalable artificial intelligence/machine learning (AI/ML) systems in a cloud environment.  \n The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimizing hardware utilization for efficient training, and performing model optimizations. As part of an interdisciplinary R\u0026D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission of reducing cancer mortality via accessible early detection. \n The role reports to the Director of Machine Learning Science. This can be a hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote. \n What you’ll do: \n \n Implement and refine DL pipelines on distributed computing platforms enhancing the speed and efficiency of DL operations including model training, data handling, model management, and inference.\n Collaborate closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines you create are perfectly aligned with scientific goals and operational needs.\n Continuously monitor, evaluate, and optimize DL model training pipelines for performance and scalability.\n Stay up to date with the latest advancements in AI, ML, and related technologies, and quickly learn and adapt new tools and frameworks, if necessary.\n Develop and maintain robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results.\n Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation pipelines.\n Act as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement.\n \n Must haves: \n \n MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development. \n 5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines. \n Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc. \n Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn. \n In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow. \n Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment. \n Understanding of containerization technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions. \n Proven track record of developing and optimizing workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume. \n Experience managing large datasets, including data storage (such as HDFS or Parquet on S3), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark). \n Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows. \n Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team. \n Excellent ability to work effectively with cross-functional teams and communicate across disciplines.  \n \n Nice to haves: \n \n Experience working with large-scale genomics or biological datasets. \n Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data. \n Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA). \n Experience with infrastructure-as-code and configuration management. \n Experience cultivating MLOps and ML infrastructure best practic","salary_min":161925,"salary_max":227325,"location":"Brisbane, California","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["tensorflow","pytorch","gpu","jax","search","llm","mlops","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/freenome/jobs/8013673002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-10-20T20:46:03Z","expires_at":"2026-06-29T14:13:52.655206Z","created_at":"2026-04-16T15:56:16.500776Z","updated_at":"2026-05-30T14:13:52.77242Z","company_name":"Freenome","company_slug":"freenome","company_logo_url":"https://www.google.com/s2/favicons?domain=freenome.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/efb7afd1-652b-44b3-b97c-2271f7ada531"},{"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","job_type":"full-time","experience_level":"mid","tags":["robotics","reinforcement-learning","jax","deep-learning","pytorch","nlp","autonomous-vehicles","generative-ai"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7309064","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-10-08T19:48:21Z","expires_at":"2026-06-29T14:04:25.978164Z","created_at":"2026-04-13T09:40:16.579726Z","updated_at":"2026-05-30T14:04:26.094772Z","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, 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 and Affirmative ","salary_min":256000,"salary_max":276000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["tensorflow","jax","agents","cloud","reinforcement-learning","alignment","llm","pytorch"],"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":"2026-06-29T14:18:20.447017Z","created_at":"2026-04-17T04:55:11.806188Z","updated_at":"2026-05-30T14:18:20.570147Z","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, 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, ","salary_min":218500,"salary_max":288000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["tensorflow","data-pipeline","pytorch","reinforcement-learning","jax","fine-tuning","mlops","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/postman/jobs/7452539003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-10-08T17:02:29Z","expires_at":"2026-06-29T14:18:20.2695Z","created_at":"2026-04-17T04:55:11.631102Z","updated_at":"2026-05-30T14:18:20.390399Z","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","job_type":"full-time","experience_level":"principal","tags":["llm","alignment","jax","gpu","pytorch","distributed-systems","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/4926227008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-09-22T16:25:42Z","expires_at":"2026-06-29T14:00:18.976521Z","created_at":"2026-04-13T09:35:57.775908Z","updated_at":"2026-05-30T14:00:19.093964Z","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":"5bad79c0-3400-4bfa-a226-20d3b6f8a77d","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Research Scientist, Foundation Model for Simulation","slug":"senior-research-scientist-foundation-model-for-simulation-7e3e28cd","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. 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As part of our work, we also initiate and foster collaborations with other research teams in Alphabet.\n In this hybrid role you will report to a Technical Lead Manager.         \n You will:  \n \n \n Conduct applied foundation model research and development\n \n Design compelling experiments by training and evaluating large deep learning models\n \n Present results to peers and leadership\n \n Write high quality code, unit tests, and documentation\n \n You have: \n \n \n Masters or PhD in deep learning in behavior and agent modeling or simulation\n Proficiency in Python\n \n 3+ years of experience with modern deep learning frameworks. Prefer JAX or TensorFlow 2; however, Pytorch is acceptable.\n Experience in generative modeling and LLMs or VLMs (e.g., Gemini, Llama, GPT)\n \n We prefer: \n Experience with:\n \n Post-training, incl. reinforcement learning\n Distillation\n Transformer models\n Autoencoders and embeddings\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 $204,000 — $259,000 USD","salary_min":204000,"salary_max":259000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","generative-ai","llm","pytorch","deep-learning","jax","tensorflow","reinforcement-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7159299","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-08-26T16:47:56Z","expires_at":"2026-06-29T14:04:27.697483Z","created_at":"2026-04-13T09:40:17.920229Z","updated_at":"2026-05-30T14:04:27.811336Z","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/5bad79c0-3400-4bfa-a226-20d3b6f8a77d"},{"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":"ABOUT xAI \n xAI’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 xAI’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 xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n xAI is an equal opportunity employer. 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You'll work with researchers, ML practitioners, and data scientists every day through GitHub, our forums, and Slack.\u003c/p\u003e\u003ch3\u003eAbout You\u003c/h3\u003e\u003cp\u003eYou have a public track record of open-source work, and you enjoy collaborating with a community out in the open on GitHub. You love open source, you're passionate about making complex technology more accessible, and you want to contribute to one of the fastest-growing ML ecosystems. If that's you, we can't wait to see your application.\u003c/p\u003e\u003ch3\u003eWhat you'll need\u003c/h3\u003e\u003cul\u003e\u003cli\u003eStrong Python skills, with experience writing clean, well-tested, maintainable library code\u003c/li\u003e\u003cli\u003eDeep hands-on experience with a modern deep-learning framework, especially PyTorch (JAX or TensorFlow a plus)\u003c/li\u003e\u003cli\u003ePractical experience with the Hugging Face open-source stack (Transformers, Datasets, Accelerate) or comparable ML libraries\u003c/li\u003e\u003cli\u003eA public track record of open-source contributions, for example merged pull requests to ML or data libraries, that we can review on GitHub\u003c/li\u003e\u003cli\u003eSolid understanding of modern machine learning and deep learning, including transformer architectures\u003c/li\u003e\u003cli\u003eExperience collaborating with a technical community in the open (GitHub issues and reviews, forums, Slack or Discord)\u003c/li\u003e\u003cli\u003eFluent written English for asynchronous collaboration across a distributed, global community\u003c/li\u003e\u003c/ul\u003e\u003ch3\u003eNice to have\u003c/h3\u003e\u003cul\u003e\u003cli\u003eExperience maintaining an open-source project\u003c/li\u003e\u003cli\u003ePrior contributions to Transformers, Datasets, Accelerate, or similar libraries\u003c/li\u003e\u003cli\u003eFamiliarity with distributed training, inference optimization, or GPU/accelerator performance work\u003c/li\u003e\u003cli\u003eExperience training or fine-tuning models at scale\u003c/li\u003e\u003c/ul\u003e\u003ch3\u003eA note on fit\u003c/h3\u003e\u003cp\u003eIf you're interested in joining us but don't tick every box above, we still encourage you to apply. We're building a diverse team whose skills, experiences, and backgrounds complement one another, and we're happy to consider where you might make the biggest impact.\u003c/p\u003e\u003ch3\u003eHow to apply\u003c/h3\u003e\u003cp\u003eAt Hugging Face we believe great AI shouldn't require a massive cluster, we build for everyone, especially the GPU-poor. And because we genuinely read every application, here's a small sign that you read this one too: start your cover letter with the words “GPU-poor and proud of it 🤗” so we know you read the full description. No trick, no catch, it just tells us a real person is on the other side.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMore about Hugging Face\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWe are actively working to build a culture that values diversity, equity, and inclusivity\u003c/strong\u003e. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWe value development.\u003c/strong\u003e You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWe care about your well-being.\u003c/strong\u003e We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWe support our employees wherever they are\u003c/strong\u003e. While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWe want our teammates to be shareholders\u003c/strong\u003e. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWe support","location":"New York, NY","workplace":"remote","job_type":"full-time","experience_level":"mid","tags":["llm","tensorflow","fine-tuning","pytorch","jax","distributed-systems","deep-learning","machine-learning"],"apply_url":"https://apply.workable.com/j/19A136F8E2","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T00:00:00Z","expires_at":"2026-06-29T14:05:08.05761Z","created_at":"2026-05-30T14:05:08.166929Z","updated_at":"2026-05-30T14:05:08.166929Z","company_name":"Hugging Face","company_slug":"hugging-face","company_logo_url":"https://www.google.com/s2/favicons?domain=huggingface.co\u0026sz=128","quality_score":60,"url":"https://aidevboard.com/job/41b12467-7321-4b10-83eb-ff61a6b6948d"}],"page":1,"per_page":20,"total":47,"total_pages":3}