{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"b1638ac7-d1b2-4de0-b991-6d17c0656bb6","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Staff Gen AI Research Scientist ","slug":"staffgenai-research-scientist-f60a2c4a","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 Air Dominance \u0026 Strike team at Anduril develops aerial and multi-domain robotic systems. The team is responsible for taking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product. The team also develops Lattice for Mission Autonomy, Anduril’s premier software platform that enables masses of Fury, Barracuda, and other first and third party robots to collaborate across various missions. We work in close coordination with specialist teams like Perception, Motion Planning, Hardware, and Test Engineering to solve some of the hardest problems facing our customers. We are looking for AI research scientists, Applied scientists and roboticists excited about creating a powerful autonomy software stack that includes computer vision, motion planning, SLAM, controls, estimation, and secure communications.   \n ABOUT THE JOB   \n We are seeking an  AI Research Scientist  to serve as a founding ML expert on our team. In this role, you will design, fine-tune, and deploy the next generation of generative AI, LLMs, and agentic systems that power our air-dominance platforms and collaborative autonomous behaviors.    \n This is a highly applied research role  (split roughly 60% applied research/experimentation and 40% hands-on coding)  focused on making state-of-the-art LLM models smaller, faster, and smarter. You will work on both offboard systems (for complex mission planning, modeling, and simulation) and onboard systems—optimizing models to run directly on power- and compute-constrained edge hardware. As an early member of this initiative, you will have significant autonomy to set the technical direction, design our data collection strategy across test sites and simulations, and directly influence how multi-agent autonomy is deployed in critical missions.   \n WHAT YOU’LL DO   \n \n Develop, pre-train, and fine-tune in-house LLMs and multimodal foundation models. Apply SOTA post-training alignment techniques (SFT, RLHF, DPO) to maximize capability while minimizing cost and footprint. \n Architect and optimize models to run directly on tactical edge compute and power-constrained hardware onboard physical assets. Optimize model latency, memory usage, and execution speed through quantization, distillation, and pruning. \n Design and implement robust agentic architectures, multi-agent coordination frameworks, and planning loops for complex, multi-domain military missions. \n Collaborate closely with computer vision, perception, and motion planning teams to build systems capable of reasoning over diverse modalities, including camera feeds, radar, telemetry, and text-based operational orders. \n Define and execute data collection strategies across physical assets, test sites, and virtual simulations. Work with AI Infrastructure engineers to build scalable evaluation frameworks that measure model performance, reliability, and safety in high-stakes environments. \n Build early-stage prototypes alongside customers, quickly iterate on feedback, and scale those prototypes into production-grade features deployed across our family of systems.   \n \n REQUIRED QUALIFICATIONS   \n \n Strong production-level coding skills in Python and deep learning frameworks (like PyTorch or JAX).  \n Hands-on experience training, fine-tuning, and evaluating LLMs, Generative AI, or multimodal models. \n A strong background in a classical technical discipline (Computer Vision, NLP, Robotics, or Speech) with 2+ years of dedicated experience focusing on generative models and modern transformer architectures. \n Experience using modern model training, alignment, and orchestration tools (e.g., Axolotl, Hugging Face, DeepSpeed, Megatron-LM, LangChain, or LlamaIndex). \n Ability to operate comfortably in a fast-paced environment, moving from ambiguous mission requirements to concrete code and functional prototypes. \n Degree (B.S., M.S., or Ph.D.) in Computer Science, Machine Learning, Robotics, Physics, Mathematics, or a related technical field. \n Eligible to obtain and maintain an active U.S. Top Secret security clearance.   \n \n PREFERRED QUALIFICATIONS   \n \n Proven track record of compiling and running deep learning models on edge accelerato","salary_min":220000,"salary_max":292000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","cloud","payments","robotics","diffusion-models","generative-ai","pytorch","nlp"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5216230007?gh_jid=5216230007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-19T16:20:23Z","expires_at":"2026-09-29T13:37:30.58848Z","created_at":"2026-08-25T18:28:19.681085Z","updated_at":"2026-08-30T13:37:30.721438Z","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/b1638ac7-d1b2-4de0-b991-6d17c0656bb6"},{"id":"741866cf-ead6-4e1c-9ab9-de92c040f40f","company_id":"4bc4e268-7a05-4a65-a162-1688af546f7e","title":"Machine Learning Intern","slug":"machine-learning-intern-5bbef37c","description":"WHAT MAKES US EPIC?\n At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.\n Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.\n ENGINEERING - SPECIAL PROJECTS\n What We Do \n The Special Projects team at Epic is responsible for executing high-impact projects that push the envelope to define the future of real-time graphics and gaming technology (The Matrix Awakens, Lumen in the Land of Nanite). In collaboration with the Unreal Engine team, we strive to put our technology and knowledge into the hands of users, empowering developers and content creators with the most powerful suite of real-time tools in the world.\n MACHINE LEARNING RESEARCH INTERNSHIP AT EPIC GAMES\n What You'll Do \n Epic Games is looking for current PhD students and recent PhD or MSc graduates (within 12 months of graduation) for paid, 6-12 month research internships with our Special Projects \u0026 Epic Research Group. You will work with our team of research scientists and engineers at the intersection of vision, language, speech processing and game development to create machine learning models in a range of areas to support game developers and improve user experience.\n In this role, you will \n \n Create machine learning models in areas including player safety, game agents (e.g. AI-backed characters, VLA agents), world/scene and game logic generation, developer tools, and improved user experience\n Work alongside our team of research scientists and engineers on applied research problems\n Own data analysis and data creation, supported by an internal team of labellers\n Design, implement, and experiment with models end to end\n Gain exposure to engineering work around deploying models at scale\n \n What we're looking for \n \n An ongoing or recently completed (within the last 12 months) PhD in Computer Science, Mathematics, AI or a related field - recent MSc graduates in the same fields will also be considered\n Experience building models or algorithms in computer vision, natural language processing, or speech/audio/acoustic processing\n Hands-on knowledge of modern deep learning methods, such as Transformers, LLM fine-tuning, and diffusion models\n Strong coding skills in Python and the standard ML stack (PyTorch, NumPy, SciPy, scikit-learn)\n Desirable: Game dev experience in either C++ or C# (including personal projects)\n \n Internship Details \n \n Duration: 6-12 months, with flexible start dates throughout 2026/2027\n Hours: full-time (40 hours/week) or part-time (20 hours/week)\n Location: Select regions within the UK, US or Canada\n Employment Authorization: You must hold an existing right to work in the UK, US or Canada for the full duration of the internship - Visa sponsorship or support is unavailable for this role\n \n This role is open to multiple locations across North America and Europe (including CA, NYC, \u0026 WA). \n This internship has a flexible start date in 2026/2027. Recruitment will be ongoing until teams find an ideal match. Applicants must be legally authorized to work in the posting location for the duration of the internship. For more information about Epic’s Early Career Program, visit epicgames.com/earlycareers . This is going to be Epic! \n Pay Transparency Information \n The expected annual base pay range(s) for this position are detailed below. Each base pay range is relevant only for individuals who are residents of or will be expected to work within the specified locale. Compensation varies based on a variety of factors, which include (but aren’t limited to) things such as skills and competencies, qualifications, knowledge, and experience. In addition to base pay, most employees are eligible to participate in Epic’s generous benefit plans and discretionary incentive programs (subject to the terms of those plans or programs). \n New York City Base Pay Range\n $139,029 — $166,834 USD \n California Base Pay Range\n $122,345 — $146,814 USD \n Washington Base Pay Range\n $111,223 — $133,468 USD \n ABOUT US\n Epic Games​ ​is a leading interactive entertainment company. For over 30 years we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarka","salary_min":111223,"salary_max":133468,"location":"BLANK,BLANK,Multiple Locations","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["diffusion-models","deep-learning","fine-tuning","pytorch","nlp","llm","computer-vision","machine-learning"],"apply_url":"https://epicgames.com/careers/jobs/6138134004?gh_jid=6138134004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-07T16:12:54Z","expires_at":"2026-09-29T13:47:27.420832Z","created_at":"2026-08-25T18:32:57.991607Z","updated_at":"2026-08-30T13:47:27.55394Z","company_name":"Epic Games","company_slug":"epic-games","company_logo_url":"https://www.google.com/s2/favicons?domain=epicgames.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/741866cf-ead6-4e1c-9ab9-de92c040f40f"},{"id":"1d0e5c45-aa5f-4a6c-9242-6092bf439e87","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Staff, ML Engineer - Scene Generation","slug":"staff-ml-engineer-scene-generation-1ba58b93","description":"About the Company   \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.   \n Meet the Team   \n Torc is marching towards its AV 3.0 strategy. The Scene Generation team builds sensor simulation based on Neural Rendering and generative models for Torc's data-driven simulator. High-quality data from novel scenarios closes the domain data gap, solving one of the biggest challenges for safe autonomous driving. The team works on state-of-the-art approaches for Neural Rendering and data generation for camera, LiDAR, and Radar sensor data, contributing to Torc's data workflow for training and validation across the complete AV stack. As Staff ML Engineer, you will lead this team, setting its technical direction and day-to-day priorities.   \n What You'll Do   \n \n Serve as the team's technical lead, owning key design decisions and driving consensus across engineers and stakeholders. \n Set and own the technical vision and roadmap for the Scene Generation team's Neural Rendering and generative modeling work, aligned to Torc's AV 3.0 strategy.\n Lead the Scene Generation team day to day, prioritizing work, unblocking engineers, and making the final call on architecture and technical approach.\n Implement and guide the team in applying the latest research advances in Neural Rendering and generative models.\n Translate cutting edge research into production quality Camera, LiDAR, and Radar sensor simulation that scales perception simulation and AV 3.0 training.\n Own the neural rendering framework end to end, from research, design, and implementation through testing, cloud integration, and deployment.\n Understand the integration of the framework in a cloud environment and automate the pipeline to scale target verification and validation of our autonomous trucks.\n Design, implement, test, and deploy shippable production quality software using disciplined software development processes, setting the technical bar for the team.\n Work within the cloud machine learning ecosystem alongside other machine learning services across the company, owning integration and scaling decisions for the team's pipeline.\n Proactively assess current capabilities to identify areas for improvement, proposing and driving solutions that align with core strategy and operations.\n Drive alignment across team interfaces to the rest of the organization, representing Scene Generation's roadmap, priorities, and constraints to peer teams and leadership.\n Mentor, coach, and grow engineers on the team, and play an active role in hiring, onboarding, and career development for the group.   \n \n What You'll Need to Succeed   \n \n Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 10+ years of experience OR Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 7+ years of experience OR PhD in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 5+ years of experience. \n Proficiency in Python and deep learning frameworks such as PyTorch.\n PhD or equivalent work experience of 8+ years in a relevant field (CS, Robotics, Electrical Engineering), with industry experience shipping production software and leading technical teams.\n Proven expertise in Neural Rendering (Neural Radiance Fields and 3D Gaussian Splatting) and generative models (Diffusion Models, Flow Matching).\n Experience with production data in the context of robotics and/or autonomous driving\n Background in Computer Vision, Computer Graphics, 3D Reconstruction, or 3D Computer Vision.\n Experience handling autonomous driving sensor data across multiple timestamps and sensor modalities, including cameras, LiDAR, and radar.\n Recognized as an expert in the discipline, conducting complex, high impact work under minimal supervision and with wide latitude for independent judgment.\n Experience with VDI and cloud-based machine learning development environments.\n Demonstrated experience leading a team or serving as a technical lead, setting direction and driving consensus across engineers and stakeholders.\n Track record of mentoring and developing engineers, including more senior individual contributors.\n Proven abili","salary_min":249600,"salary_max":299500,"location":"Ann Arbor, MI","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["computer-vision","deep-learning","diffusion-models","autonomous-vehicles","robotics","computer-graphics","payments","gpu"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8651134002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-04T16:54:57Z","expires_at":"2026-09-29T13:36:08.377363Z","created_at":"2026-08-25T18:27:50.585738Z","updated_at":"2026-08-30T13:36:08.509631Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1d0e5c45-aa5f-4a6c-9242-6092bf439e87"},{"id":"ba5208f5-0ab3-485a-bfe8-e7efde11598b","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Scientist II / Senior ML Scientist, Cofolding and Structure-Aware ML","slug":"scientist-ii-senior-ml-scientist-cofolding-and-structure-aware-ml-31ff5fdc","description":"Your Impact at LILA \n Lila Sciences is seeking a Machine Learning Scientist, Cofolding and Structure-Aware ML to train next-generation cofolding models for drug discovery. This role is focused on improving models that reason over proteins, ligands, binding context, and experimental data, potentially using contrastive learning and related representation-learning approaches.\n This person should have direct experience training modern scientific ML models, not only using pretrained systems. You will work with ML researchers, computational chemists, computational biophysicists, data engineers, and drug discovery teams to develop models that learn from DEL and related datasets, connect molecular and protein context, and improve AI-driven discovery decisions.\n The models developed in this role should produce outputs that medicinal and computational chemists as well as biophysicists can interrogate, validate, and use in downstream agent-driven discovery decisions.\n What You'll Be Building \n \n Train and evaluate cofolding models for protein-ligand and related molecular discovery applications.\n Use contrastive learning, representation learning, self-supervised learning, or related methods where they help improve cofolding models trained on molecules, proteins, structures, and experimental readouts.\n Develop modeling approaches that make DEL data more useful for learning binding, enrichment, selectivity, and structure-activity signals.\n Build and evaluate models informed by Boltz, AlphaFold-style cofolding, equivariant GNNs, and related structure-aware ML methods.\n Design training objectives, including contrastive, self-supervised, or multimodal objectives, that connect ligands, proteins, structures, assays, simulations, and experimental data.\n Build rigorous evaluation frameworks that distinguish meaningful molecular learning from dataset artifacts, leakage, or spurious correlations.\n Collaborate with data and platform teams to define datasets, labels, negatives, controls, and metadata needed for model training.\n Partner with computational chemistry and biophysics teams to connect model outputs to physically and chemically meaningful hypotheses.\n Work with low-data learning scientists to identify which DEL, assay, simulation, or structural data would most improve model performance in focused chemical spaces.\n Work with research engineers to scale training, inference, and evaluation workflows.\n Help expose trained models and model-derived capabilities as tools for scientists and AI agents.\n \n What You'll Need to Succeed \n \n PhD or equivalent experience in machine learning, computational biology, computational chemistry, bioinformatics, computer science, or a related field.\n Hands-on experience training deep learning models for molecular, protein, structural biology, or scientific data applications.\n Experience with contrastive learning, representation learning, self-supervised learning, or multimodal learning.\n Familiarity with DEL or related selection, enrichment, screening, or molecular assay datasets.\n Experience with protein-ligand modeling, cofolding, structure prediction, geometric deep learning, or structure-aware molecular ML.\n Practical experience with PyTorch, JAX, or an equivalent ML framework.\n Ability to design careful experiments, ablations, and evaluations for scientific ML models.\n Strong understanding of data quality, leakage risks, negative construction, and benchmark design.\n Ability to collaborate across ML, data, computational science, and drug discovery functions.\n \n Bonus Points For \n \n Hands-on experience with DEL data.\n Drug discovery experience, especially in protein-ligand modeling or molecular optimization contexts.\n Experience with Boltz, AlphaFold or AlphaFold-derived methods, equivariant GNNs, diffusion models, protein language models, or molecular encoders.\n Experience training or extending cofolding, protein-ligand, protein-protein, structure prediction, diffusion, or geometric deep learning models.\n Experience with distributed model training and large-scale scientific data pipelines.\n Familiarity with active learning or closed-loop molecular design.\n Experience integrating ML models into agentic scientific workflows.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; inter","salary_min":228000,"salary_max":358000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","data-pipeline","search","deep-learning","diffusion-models","agents"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4340151009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-03T13:42:07Z","expires_at":"2026-09-29T13:48:24.195375Z","created_at":"2026-08-25T18:33:17.006403Z","updated_at":"2026-08-30T13:48:24.321424Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ba5208f5-0ab3-485a-bfe8-e7efde11598b"},{"id":"e57cf48d-3756-4016-8e50-400a76bbaa5d","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Visual AI","slug":"staff-machine-learning-engineer-computer-vision-147d8a7f","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n About the Team: \n Hundreds of millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love.\n Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development to power the platform. Labs works across a broad variety of AI/ML initiatives, including LLMs/VLM, agent design, core computer vision, multimodal representation learning, visual generative modeling, recommender systems, graph learning, and more. This is the group that develops the foundation AI models that fully leverage the hundreds of billions of Pins and the associated knowledge graphs, and ships new product capabilities to fully utilize these technologies.\n We are currently hiring for the Visual team in Labs, which develops Pinterest's foundation visual models. In this role, you'll work with Pinterest's rich visual-text dataset to train large-scale VLMs, encoders, and diffusion models from scratch that are continuously shipped to production to power visualization and search capabilities. The team is subdivided into two pods, which share pretraining datasets, training and RL infrastructure, and generally co-develop our visual modeling ecosystem. The visual understanding pod builds the core visual embeddings and token models such as PinCLIP and deeply integrates them with the VLM/LLM/agent ecosystem at Pinterest. The visual generative pod builds Pinterest Canvas , our production image editing and generation model used across our assistant, visualization, and monetization products. Labs is staffed to be a highly collaborative environment where research scientists, ML engineers, product builders, and infrastructure engineers all work directly together, so that the team can plug into any technical effort at the company to support fast productionization.\n What you’ll do: \n \n Build state-of-the-art visual encoders, VLMs, and diffusion models that power Pinterest's visual AI capabilities\n Experiment with billion-scale image datasets, backed by large-scale GPU computing.\n Build flexible visual reasoning tools such as composed image retrieval, promptable image feature computation, instruction-tuned embedding and generative editing models, and more.\n Read research papers, participate in group discussions, and directly participate in brainstorming the company's overall AI strategy.\n Help construct data agents to build training data that can be shared across multimodal representation, composed image retrieval, image-editing generation, and visual language modeling.\n Collaborate directly with product engineers and infrastructure engineers to ship new capabilities in the core product.\n Publish and share your work through conferences like CVPR and KDD, paper submissions, and blog posts.\n Mentor junior researchers and research interns within the Pinterest Labs organization.\n Collaborate across a team situated across San Francisco, Seattle, NYC, and remote roles.\n \n What we’re looking for: \n \n Research engineers and scientists with experience building and training large scale vision models of all categories.\n Experience with multimodal representations and visual language modeling is strongly preferred.\n A track record of research contributions (e.g., publications, open-source work) and/or shipping ML models to production.\n Hands-on experience with large-scale model training and modern deep learning frameworks (e.g., PyTorch).\n Strong collaboration skills and a demonstrated ability to work effectively in a small, fast-moving team.\n M.S. or PhD in Machine Learning or related academic areas, or equivalent work experience.\n Experience using AI-accelerated research tooling akin to auto-research, data agents, etc","salary_min":189308,"salary_max":389753,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["diffusion-models","llm","computer-vision","pytorch","pre-training","search","deep-learning","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8015537","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T17:51:37Z","expires_at":"2026-09-29T13:38:55.670156Z","created_at":"2026-07-15T14:10:33.975738Z","updated_at":"2026-08-30T13:38:55.805682Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e57cf48d-3756-4016-8e50-400a76bbaa5d"},{"id":"cb6610fe-3f66-4e07-b765-55c7b851bcf3","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff ML Engineer, Generative Model Performance \u0026 Efficiency","slug":"staff-ml-engineer-generative-model-performance-efficiency-d4d7a93b","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 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 Simulator Team at Waymo builds state-of-the-art simulations of realistic environments for testing, training, and validation of the Waymo Driver. Our team is a diverse, and collaborative group of machine learning (ML) engineers, software engineers, and ML research engineers. We develop industry-leading simulation solutions using advanced generative and reconstructive ML algorithms, to model the real world, encompassing realistic agents, roads, traffic systems, weather, and the full sensor suite (Camera, Lidar, Radar).\n To accelerate the fidelity, scalability, controllability, and richness of our simulations, we are pushing the frontiers of 3D world modeling. We leverage state-of-the-art ML technologies trained on large-scale datasets to create dynamic and semantically rich virtual worlds, directly impacting the development and validation of the Waymo Driver.\n In this role, you will report to a Senior Staff Engineering Manager\n  \n You will: \n \n Analyze model architectures and identify bottlenecks in training and inference performance (e.g., memory bandwidth, compute, communication).\n Apply and develop techniques such as quantization (e.g., FP8, INT4), pruning, knowledge distillation, and efficient attention mechanisms.\n Optimize model code for specific hardware accelerators (TPUs, GPUs), leveraging compiler features and low-level libraries (e.g., XLA).\n Experiment with different model partitioning and sharding strategies (e.g., data, tensor, pipeline parallelism, expert parallelism) to improve scalability and efficiency.\n Design and implement low-latency, high-throughput serving solutions for generative models and optimize training pipelines to reduce training time.\n Build and maintain tools for performance analysis, profiling (e.g., xprof), and debugging of ML models.\n \n  \n You have: \n \n MS or PhD in Computer Science, Machine Learning, Robotics, or a related field.\n 5+ years of experience with deep learning architectures (especially Transformers, Diffusion Models, MoEs), algorithms, and optimization techniques.\n Proficiency in JAX, Flax, and potentially TensorFlow/PyTorch.\n Expertise in using profiling tools (e.g., XProf, Perfetto, NVIDIA Nsight) to diagnose performance issues in ML workloads.\n Hands-on experience with quantization, pruning, distillation, and other model compression methods.\n Strong programming skills in Python and potentially C++, with experience in software development best practices.\n \n  \n We prefer: \n \n Knowledge of TPU and GPU architectures and how to optimize code for them.\n Familiarity with ML compilers like XLA and an understanding of how they translate high-level code to efficient hardware instructions.\n Understanding of concepts related to training and serving models across multiple devices and machines.\n Experience contributing to frameworks and libraries that improve training speed and scalability (e.g., JAX, Gemax, XManager).\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","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["search","robotics","autonomous-vehicles","tensorflow","pytorch","diffusion-models","deep-learning","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8027424","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-29T22:08:47Z","expires_at":"2026-09-29T13:35:10.765757Z","created_at":"2026-06-30T14:04:23.960313Z","updated_at":"2026-08-30T13:35:10.900081Z","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/cb6610fe-3f66-4e07-b765-55c7b851bcf3"},{"id":"adcfc258-e967-46de-8d48-76960b1055f0","company_id":"cfda17be-0e63-4ab8-81e8-2132d849cd01","title":"Member of Technical Staff - Research Engineer","slug":"member-of-technical-staff-research-engineer-0876ca83","description":"About Black Forest Labs \n We're the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’re creating the generative models that power how people make images and video—tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started.\n Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.\n Why This Role \n Large-scale training is where research ideas become real, and where many of the hardest problems are no longer cleanly separated into “research” or “engineering.” A promising architecture only matters if we can train it stably, efficiently, and correctly across large GPU fleets.\n In this role, you will be embedded in production training and help where the hardest systems and performance problems arise: attention performance, custom kernels, low-precision training, profiling, memory behavior, data movement, distributed training stability, and throughput regressions. You will work directly with researchers, but your output will often be code, measurements, kernels, debugging tools, and training-system changes that make better research possible.\n We are open to a range of seniority for this role. The common thread is deep technical ownership: you should be able to make progress in ambiguous training-system problems, verify your results, and own the outcome.\n What You’ll Work On \n \n Improve the performance, reliability, and numerical stability of production training runs for large multimodal generative models\n Profile full training steps across model code, attention, kernels, data loading, encoders, communication, optimizer steps, checkpointing, and memory pressure\n Implement and validate GPU-level optimizations: fused kernels, attention paths, low-precision matmuls, quantization kernels, CUDA/Triton/CuTe/CUTLASS experiments, and no-compile alternatives where they make sense\n Push lower-precision training forward, including FP8 / MXFP8 / FP4-style paths, weight and activation quantization, accumulation choices, convergence risk, and quality tradeoffs against baseline training runs\n Work with researchers to translate architecture changes into efficient training implementations, and help distinguish real model-quality progress from changes that only look good in a microbenchmark\n Debug distributed training failures: NaNs, loss spikes, silent numerical drift, memory leaks, stragglers, bad nodes, NCCL issues, and throughput cliffs\n Build benchmarking and profiling harnesses that make performance claims trustworthy across hardware, shapes, sequence lengths, and training configurations\n Help the training team move quickly when an urgent bottleneck appears, while turning repeated failures into better abstractions and tools\n \n What We’re Looking For \n \n Experience working deeply on large-scale training systems, ideally as part of a training group working closely with researchers\n Strong PyTorch fluency, including comfort reading and modifying low-level training code rather than only using high-level APIs\n Experience with distributed training concepts such as FSDP, tensor/model/context/sequence parallelism, activation checkpointing, NCCL, and overlapping compute and communication\n Hands-on experience improving training throughput, memory footprint, or stability in real training runs\n Experience profiling GPU workloads with tools like Nsight Systems, Nsight Compute, torch profiler, trace viewers, or custom telemetry\n Practical GPU performance judgment: you may use modern coding agents and tools as much as you want, but you need the understanding to verify correctness, numerical behavior, and performance, and to own the result\n Understanding of low-precision training and quantization tradeoffs: FP8, MXFP8, FP4/NVFP4-style formats, scaling, accumulation, numerical validation, and convergence risk\n Good research judgment: you can partner with researchers on ablations, understand what the measurements do and do not prove, and keep optimization work tied to model-quality outcomes\n Comfortable operating in ambiguity: sometimes the task is a clean implementation, sometimes it is a production fire, and sometimes it is figuring out which of three plausible explanations is actually true\n \n We'd be especially excited if you: \n \n Have supported or co-owned training for a frontier foundation model that shipped or reached a major release\n Have written or substantially improved forward/backward GPU kernels, or have shown you can make progress on kernel-level work with strong measurement and validation discipline\n Have worked on attention performance, variable sequence length training, non-standard attention patterns\n Have experience on Hopper or Blackwell-class","salary_min":180000,"salary_max":290000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","diffusion-models","pytorch","llm","gpu","search","generative-ai","research"],"apply_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5286037008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-29T19:48:39Z","expires_at":"2026-09-26T13:44:15.686613Z","created_at":"2026-06-30T14:12:56.164349Z","updated_at":"2026-08-27T13:44:15.809245Z","company_name":"Black Forest Labs","company_slug":"black-forest-labs","company_logo_url":"https://www.google.com/s2/favicons?domain=blackforestlabs.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/adcfc258-e967-46de-8d48-76960b1055f0"},{"id":"a8d08f57-0c6f-40d4-b42c-4fa9061feaed","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Member of Technical Staff, Inference ","slug":"member-of-technical-staff-inference-86b5008f","description":"Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for an inference runtime engineer to push the boundaries of what's possible in LLM and diffusion model serving. Models grow larger. Architectures shift: mixture-of-experts, multimodal, agentic. Every breakthrough demands innovations on the inference engine itself. You'll work at the core of vLLM, optimizing how models execute across diverse hardware and architectures. Your work will directly impact how the world runs AI inference. \n\n\n\n\nSKILLS AND QUALIFICATIONS\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, or similar.\n\n - Deep understanding of transformer architectures and their variants.\n\n - Strong programming skills in Python with experience in PyTorch internals.\n\n - Experience with LLM inference systems (vLLM, TensorRT-LLM, SGLang, TGI).\n\n - Ability to read and implement model architectures and inference techniques from research papers.\n\n - Demonstrate the ability to contribute performant and maintainable code and debug in complex ML codebases.\n\nPreferred qualifications:\n\n - Deep understanding of KV-cache memory management, prefix caching, and hybrid model serving.\n\n - Familiarity with RL frameworks and algorithms for LLMs.\n\n - Experience with multimodal inference (audio/image/video/text).\n\n - Contributions to open-source ML or system infrastructure projects.\n\nBonus points if you have:\n\n - Implemented core features in vLLM or other inference engine projects.\n\n - Contributed to vLLM integrations (verl, OpenRLHF, Unsloth, LlamaFactory, etc).\n\n - Written widely-shared technical blogs or side projects on vLLM or LLM inference.\n   \n   \n\n\nLOGISTICS\n\n - Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.\n\n - Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\n\n - Visa sponsorship: We sponsor visas on a case-by-case basis.\n\n - Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","reinforcement-learning","diffusion-models","agents","pytorch","mlops","research","inference"],"apply_url":"https://jobs.ashbyhq.com/inferact/43c0ca54-fcf5-41fa-83a1-38800c75ccc0/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-18T18:55:18.703Z","expires_at":"2026-09-29T13:41:27.162539Z","created_at":"2026-06-28T14:10:47.134304Z","updated_at":"2026-08-30T13:41:27.292841Z","company_name":"Inferact","company_slug":"inferact","company_logo_url":"https://www.google.com/s2/favicons?domain=inferact.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a8d08f57-0c6f-40d4-b42c-4fa9061feaed"},{"id":"edd81527-9709-4b01-8b95-c78ef07b4bc1","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer, Simulation Evaluation","slug":"senior-machine-learning-engineer-simulation-evaluation-2b2339ea","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 Challenge \n Waymo’s simulator is one of the most complex virtual environments ever built. It blends deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver. The Simulator Evaluation team faces the ultimate data challenge: How do you mathematically prove that a virtual world is \"real\" ?\n We are seeking visionary machine learning engineers and researchers to architect the scalable deep learning systems, novel data workflows, and eval tools that power our research roadmap. In this role, you will pioneer the machine learning and generative vision paradigms required to define and measure the realism of our multimodal world models. Your work will define the state of the art for autonomous simulation, directly steering our research trajectory and the capabilities of the Waymo Driver.\n You will: \n \n Lead the design, development and deployment of cutting-edge evaluation approaches to assess realism of state-of-the-art multimodel world models and generative systems for simulation use cases at Waymo.\n Architect and implement robust and scalable machine learning pipelines for tuning, evaluating, and deploying large-scale discriminator models for the purposes of simulator realism evaluation. \n Evaluate open-source and production-ready video generation techniques that measure realism (e.g. temporal stability, multi-modal consistency, geometric discrepancy, condition following, etc.)\n Apply vision language models to evaluate semantic understanding and controllability across our world simulation products.\n Collaborate with research teams across Waymo and Alphabet to integrate advancements in 4D world modeling and generative AI into production systems.\n Mentor engineers on the team and provide technical guidance on architecture and execution.\n \n You have: \n \n Bachelor's, Master's, or PhD in computer science, machine learning, robotics, or a related field.\n Five or more years of experience in machine learning engineering or applied deep learning, supported by a portfolio of shipped products or peer-reviewed publications.\n Proficient programming skills in Python and hands-on experience with modern machine learning frameworks such as Jax, Flax, or PyTorch.\n Experience designing and implementing evaluation frameworks for complex systems or machine learning models.\n \n We prefer: \n \n Track record of training large-scale generative models (diffusion models, flow matching, vision language models, etc.)\n A PhD and demonstrated success delivering machine learning products focused on 3D generative models, world models, or video generation.\n Experience simulating sensor data, including camera, lidar, and radar, or modeling semantic scenes.\n Experience developing autonomous systems, robotics software, or autonomous vehicle simulations.\n Experience training and optimizing large-scale models on GPU or TPU clusters for efficient production serving.\n Professional experience writing C++ for high-performance production systems.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","generative-ai","deep-learning","robotics","pytorch","diffusion-models","machine-learning","evaluation"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8001797","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-13T17:49:43Z","expires_at":"2026-09-29T13:35:06.515878Z","created_at":"2026-06-28T14:04:26.926315Z","updated_at":"2026-08-30T13:35:06.659952Z","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/edd81527-9709-4b01-8b95-c78ef07b4bc1"},{"id":"5def081b-793e-405f-844f-117748963cdc","company_id":"4bafa9b3-1ed9-4f7e-9920-f618b1ac0b15","title":"Staff Software Engineer (AI)","slug":"staff-ai-engineer-80afc06b","description":"The Team + The Role\n Our Emerging Team is focused on building AI Products for our product experience (PX) platform. We build from the ground up to explore, prototype, and ship AI-native experiences that change how software teams understand and serve their users. This is not an AI layer added to existing product; it is a deliberate bet on what product intelligence looks like next. The team operates with high autonomy, moves quickly, and builds products without clear precedents.\n As a Staff Software Engineer (AI), you will sit at the intersection of deep technical capability and strong product judgment. You will design and build production-grade AI systems, including RAG pipelines, agentic workflows, and LLM-powered features, while making clear tradeoffs across prompting, fine-tuning, architecture, evaluation, and deployment. You will also partner closely with product, design, and engineering stakeholders to frame the right problems and communicate technical decisions clearly.\n This role is based in our New York office.\n What this looks like day-to-day\n \n Applied AI systems: Design and build AI-native systems, including RAG pipelines, agentic workflows, and LLM-powered product features. You will take ideas from prototype through production and ensure they can support real users.\n Model strategy: Make principled decisions about when to prompt, when to fine-tune, and when to use a different technical approach entirely. You will explain those tradeoffs clearly to engineers and non-engineers.\n Evaluation and guardrails: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and identifying hallucinations early. You will implement guardrails that hold up under real-world usage and load.\n Productionize AI ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will ensure AI systems are observable, performant, and production-ready.\n Full-stack delivery: Contribute across the stack when needed to get complete AI products in front of users. This team ships products, not just models, and you will help close the gap between technical capability and user experience.\n Product partnership: Partner closely with product and design to frame problems well before implementation begins. You will push back when the framing is wrong and help the team stay focused on what is worth building.\n Technical leadership: Stay current on the research and tooling landscape, including transformers, diffusion architectures, orchestration frameworks, and emerging agent patterns. You will bring relevant advances back to the team and help raise the technical bar.\n \n Who You Are\n Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.\n You're a builder, not a maintainer.\n You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great Staff AI Software Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.\n You're AI-curious - genuinely.\n You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.\n Must-haves\n \n Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.\n Demonstrated ability to set technical direction for AI systems across teams, establish architectural patterns, make foundational model strategy decisions, and raise the bar for AI engineering quality.\n Experience owning outcomes across team boundaries, including identifying capability gaps, driving alignment across engineering and product, and influencing how a broader organization approaches AI.\n Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.\n Solid understanding of modern model architectures, including transformers and diffusion models, with the ability to make informed decisions about when and how to apply them.\n Production Productionize AI experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in live environments.\n Strong full-stack fundamentals and comfort working across backend and frontend systems to ship complete, user-facing AI products.\n Exceptional communication skills, with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.\n Demonstrated product judgment and the","salary_min":300000,"salary_max":325000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["diffusion-models","alignment","agents","generative-ai","mlops","data-pipeline","llm","rag"],"apply_url":"https://job-boards.greenhouse.io/pendo/jobs/8533499002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-05T18:04:00Z","expires_at":"2026-09-29T13:49:17.125345Z","created_at":"2026-05-06T14:25:29.842467Z","updated_at":"2026-08-30T13:49:17.253861Z","company_name":"Pendo","company_slug":"pendo","company_logo_url":"https://www.google.com/s2/favicons?domain=pendo.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5def081b-793e-405f-844f-117748963cdc"},{"id":"45002517-2a02-492f-89c0-a43d9b4b0ed6","company_id":"4bafa9b3-1ed9-4f7e-9920-f618b1ac0b15","title":"Sr. Software Engineer (AI)","slug":"sr-ai-engineer-594a2514","description":"The Team + The Role\n Our Emerging Team is focused on building AI Products for our product experience (PX) platform. We build from the ground up to explore, prototype, and ship AI-native experiences that change how software teams understand and serve their users. This is not an AI layer added to existing product; it is a deliberate bet on what product intelligence looks like next. The team operates with high autonomy, moves quickly, and builds products without clear precedents.\n As a Sr. Software Engineer (AI), you will sit at the intersection of deep technical capability and strong product judgment. You will design and ship applied AI systems, including RAG pipelines, agentic workflows, and LLM-powered features, from prototype through production. You will make principled technical decisions, evaluate model behavior rigorously, and communicate tradeoffs clearly to engineers and non-engineers alike.\n This role is based in our New York office.\n What this looks like day-to-day\n \n Applied AI systems: Design and build AI systems including RAG pipelines, agentic workflows, and LLM-powered features. You will take work from prototype through production and ensure it can hold up in real customer environments.\n Technical decision-making: Make principled decisions on when to prompt, when to fine-tune, and when to use a different tool entirely. You will explain these tradeoffs clearly so the team can move quickly without sacrificing quality.\n Model evaluation: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and catching hallucinations early. You will implement guardrails that can withstand real-world load and production use.\n Productionize AI ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will help ensure AI systems are observable, efficient, and dependable in production.\n Full-stack product shipping: Contribute across the stack when needed because this team ships products, not just models. You will work across backend and frontend to get AI-powered experiences in front of users.\n Product partnership: Partner closely with product and design to frame problems well before writing code. You will push back when the framing is wrong and help the team focus on what should be built, not just what can be built.\n Research and tooling awareness: Stay current on the research and tooling landscape, including transformers, diffusion architectures, orchestration frameworks, and emerging agent patterns. You will bring relevant advances back to the team and apply them thoughtfully.\n \n Who You Are\n Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.\n You're a builder, not a maintainer.\n You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great Sr. AI Software Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.\n You're AI-curious - genuinely.\n You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.\n Must-haves\n \n Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.\n Strong technical depth in system design, including choosing the right architecture, identifying failure modes early, and making tradeoffs that hold up across the product lifecycle.\n Experience owning technical quality beyond your own features, including setting standards, catching problems in review, and improving shared infrastructure and tooling.\n Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.\n Solid understanding of modern model architectures, including transformers and diffusion models, with the judgment to decide when and how to apply them.\n Production AI experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in a live environment.\n Strong full-stack fundamentals with the ability to work across backend and frontend systems to ship complete, user-facing AI products.\n Exceptional communication skills with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.\n Demonstrated product thinking, including the ability to ask whether something should be built before deciding how to build it.\n \n Nice-to-haves\n \n Experience fine-tuning founda","salary_min":250000,"salary_max":275000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","generative-ai","mlops","diffusion-models","alignment","llm","data-pipeline","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/pendo/jobs/8533495002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-05T18:03:52Z","expires_at":"2026-09-29T13:49:17.020924Z","created_at":"2026-05-06T14:25:29.754022Z","updated_at":"2026-08-30T13:49:17.155509Z","company_name":"Pendo","company_slug":"pendo","company_logo_url":"https://www.google.com/s2/favicons?domain=pendo.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/45002517-2a02-492f-89c0-a43d9b4b0ed6"},{"id":"a0e98eda-c003-4a5e-8ea7-a74bd17891e0","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"ML Scientist I / II, Foundation Models for Life Sciences","slug":"ml-scientist-i-ii-foundation-models-for-life-sciences-1bd7e0ae","description":"Your Impact at Lila \n Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team builds foundation models that learn across biological sequence, molecular structure, and experimental data to power automated scientific discovery across Lila's life science domains.\n We are seeking a Scientist I or II to work on structure prediction and co-folding. The team's current emphasis is protein–protein and complex prediction in support of antibody and biologics design, and on making those predictions good enough to drive real experimental decisions. You will contribute across problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine.\n This is an IC role for someone building deep expertise in structure-aware generative AI for biology. You will own research sub-problems end to end, collaborate closely with experimental scientists to close the computational–experimental loop, and contribute to Lila's presence in the broader scientific community.\n What You'll Be Building \n \n Train and evaluate structure prediction and co-folding models for protein complexes, protein–protein interactions, and related biomolecular systems\n Build and extend models informed by AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods\n Build rigorous evaluation frameworks to ensure model generalization to challenging de novo design problems\n Scale training, inference, and evaluation workflows across large GPU clusters\n Be part of the end-to-end ML process within Lila's “Lab-in-the-Loop” lifecycle: shape data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance\n Contribute to adjacent foundation model research where it strengthens the structural work, including biological sequence design and multimodal scientific reasoning\n Translate biological questions into well-defined ML problems and interpret model outputs alongside wet-lab scientists, structural biologists, and computational biologists\n Support research quality and methodology standards within the foundation models program\n \n What You’ll Need to Succeed \n \n PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field (or Master's with equivalent research experience)\n Hands-on experience training deep learning models on molecular, protein, or structural data\n Strong foundation in generative model architectures and training, with demonstrated ability to design careful experiments, ablations, and evaluations\n Ability to formulate and execute research independently, from problem definition through experimentation\n Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related)\n Experience collaborating with experimental scientists or working with biological/chemical data\n Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with GPU-based training workflows\n \n Bonus Points For \n \n Experience training or extending co-folding, structure prediction, protein–protein, or diffusion deep learning models\n Experience with AlphaFold or AlphaFold-derived methods (e.g., Boltz, Protenix), RFdiffusion, or protein language models\n Antibody, biologics, or protein design experience, including structure-guided optimization\n Familiarity with distributed training infrastructure and large-scale scientific data pipelines\n Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications\n Experience with active learning loops or closed-loop experimental workflows\n Experience integrating ML models into agentic scientific workflows\n High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAI, Nature Methods, Nature Biotechnology, or equivalent)\n \n  \n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $176,000 — $304,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve hum","salary_min":176000,"salary_max":304000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","gpu","generative-ai","pytorch","distributed-systems","data-pipeline","tensorflow","diffusion-models"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4222051009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-28T10:54:46Z","expires_at":"2026-09-29T13:48:21.768015Z","created_at":"2026-04-30T05:57:56.610453Z","updated_at":"2026-08-30T13:48:21.897497Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a0e98eda-c003-4a5e-8ea7-a74bd17891e0"},{"id":"74f12a4d-02f0-4d71-8ebc-3b8ab4b99aec","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Principal, Machine Learning Engineer","slug":"principal-machine-learning-engineer-f0ad90b4","description":"Your Impact at LILA \n Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team builds foundation models that learn across biological sequence, molecular structure, and experimental data to power automated scientific discovery across Lila's life science domains.\n We are seeking a Senior or Principal Scientist to set the direction for our work on structure prediction and co-folding. The team's current emphasis is protein–protein and complex prediction in support of antibody and biologics design, and on making those predictions good enough to drive real experimental decisions. You will own models end to end, from problem formulation and architecture through training at scale, evaluation, and integration into Lila's closed-loop discovery engine.\n This is a high-impact IC role for someone operating at the frontier of structure-aware generative AI for biology. You will shape the technical agenda for structural foundation model research, collaborate closely with experimental scientists to close the computational–experimental loop, and represent Lila's work to the broader scientific community.\n What You'll Be Building \n \n Drive research on structure prediction and co-folding models for protein complexes, protein–protein interactions, and related biomolecular systems\n Design, train, and evaluate models that advance the state of the art in AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods\n Set the evaluation bar for the program, building frameworks that establish model generalization to challenging de novo design problems\n Own training, inference, and evaluation at scale across large GPU clusters\n Shape the end-to-end ML process within Lila's \"Lab-in-the-Loop\" lifecycle: steer data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance\n Extend into adjacent foundation model research where it strengthens the structural work, including biological sequence design and multimodal scientific reasoning\n Translate complex biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists, structural biologists, and computational biologists\n Advance research standards and methodology within the foundation models program, contributing insights that influence approaches across adjacent teams\n Represent Lila's foundation model research externally through publications at premier venues, conference presentations, and community engagement\n \n What You’ll Need to Succeed \n \n PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field\n Demonstrated ability to formulate and drive research programs independently, from problem definition through publication and deployment\n Fluency across ML and at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related), with experience designing computational experiments grounded in biological reality\n Strong track record of cross-functional collaboration with experimental scientists, translating between ML and biology\n Expertise in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with large-scale distributed training infrastructure (AWS, GCP, or on-prem clusters)\n \n Bonus Points For \n \n Strong expertise in structure prediction, co-folding, geometric deep learning, or structure-aware molecular ML, with a track record of training these models\n Experience with AlphaFold or AlphaFold-derived methods (e.g., Boltz, Protenix), RFdiffusion, or protein language models\n Experience in computational protein design, particularly antibody and nanobody engineering\n Strong expertise in generative model architectures and training, with hands-on experience training models on distributed infrastructure\n Experience designing biological sequences or molecular structures with demonstrated wet-lab validation\n Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications\n Experience with agentic frameworks or active learning loops in scientific contexts\n Multiple high-impact first-author or senior-author publications, or open-source contributions in AI for Science, at premier venues (NeurIPS, ICML, ICLR, AAAI, Nature Methods, Nature Biotechnology, or equivalent)\n \n  \n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships ","salary_min":252000,"salary_max":374000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["diffusion-models","agents","generative-ai","tensorflow","pytorch","deep-learning","gpu","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4222224009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-28T10:54:30Z","expires_at":"2026-09-29T13:48:22.338559Z","created_at":"2026-04-30T05:57:56.631368Z","updated_at":"2026-08-30T13:48:22.521558Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/74f12a4d-02f0-4d71-8ebc-3b8ab4b99aec"},{"id":"ac941669-cf19-44b6-80e7-a50e3d179fb9","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior / Principal ML Scientist, Foundation Models for Life Sciences","slug":"senior-principal-ml-scientist-foundation-models-for-life-sciences-4217d767","description":"Your Impact at LILA \n Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), Machine Learning engineers build and operate the systems that turn foundation models over biological sequence, molecular structure, and experimental data into production capabilities powering automated scientific discovery across Lila's life science domains.\n We are seeking a Principal ML Engineer to design, build, and scale the ML infrastructure behind those models. Much of the team's current work is in structure prediction and co-folding for antibody and biologics design, alongside sequence design and multimodal scientific reasoning. You will own critical systems end to end, from training pipelines and distributed compute to model deployment and integration into Lila's closed-loop discovery engine.\n This is a high-impact IC role for someone who operates at the intersection of ML systems engineering and life science applications. You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational–experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities.\n What You'll Be Building \n \n Design, build, and optimize large-scale training pipelines for structure prediction, co-folding, and other generative models on biological and chemical data, including distributed training across GPU clusters\n Own production ML systems end to end: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows\n Architect ML infrastructure that supports rapid iteration across structure prediction, sequence design, and multimodal scientific reasoning workloads\n Make structure prediction and co-folding models fast and cheap enough to run at campaign scale, where inference volume is often the bottleneck on scientific throughput\n Drive the engineering side of Lila's \"Lab-in-the-Loop\" lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows\n Define and advance ML engineering standards, tooling, and best practices across the AI organization\n Collaborate with AI scientists to translate research prototypes into robust, scalable production systems, bridging the research-to-deployment gap\n \n What You’ll Need to Succeed \n \n Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience)\n Extensive hands-on experience building and operating production ML systems at scale\n Deep expertise in distributed training infrastructure, including experience with large-scale GPU clusters (AWS, GCP, or on-prem)\n Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices\n Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance\n Demonstrated ability to drive technical direction for ML infrastructure independently, from architecture through implementation\n Track record of cross-functional collaboration with research scientists, translating between ML methodology and engineering execution\n \n Bonus Points For \n \n Experience building training or inference infrastructure for generative models applied to biological sequences, molecular structures, or scientific data\n Experience supporting structure prediction or co-folding workloads, including AlphaFold-derived methods (e.g., Boltz, Protenix), diffusion models, or protein language models\n Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows\n Contributions to open-source ML tools, frameworks, or infrastructure projects\n Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or nucleic acid design)\n Experience with model evaluation frameworks for scientific applications where ground truth is sparse or delayed\n \n  \n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local ma","salary_min":268000,"salary_max":384000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["diffusion-models","generative-ai","gpu","mlops","distributed-systems","pytorch","tensorflow","agents"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4222034009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-27T21:00:05Z","expires_at":"2026-09-29T13:48:25.722844Z","created_at":"2026-04-30T05:57:57.291691Z","updated_at":"2026-08-30T13:48:25.852499Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ac941669-cf19-44b6-80e7-a50e3d179fb9"},{"id":"60e8de00-2b5a-4ee6-bdb3-1037d5268168","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior, Machine Learning Engineer - End-to-End","slug":"senior-machine-learning-engineer-end-to-end-1a34ca94","description":"About the Company  \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.\n A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. \n Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. \n Meet the Team: As a Senior Machine Learning Engineer – End-to-End (E2E), you will develop and scale learning-based systems that connect multi-modal perception inputs to driving behavior, enabling safe, efficient, and human-like autonomy for real-world freight operations. You’ll work at the intersection of perception, prediction, and planning, contributing to unified learning pipelines that operate in closed-loop environments. This role focuses on owning meaningful portions of the E2E stack, improving model performance at scale, and driving iteration through data, experimentation, and cross-functional collaboration. This is a hands-on engineering role focused on execution, iteration, and delivery. What You’ll Do \n \n Own development and delivery of End-to-End ML models that map multi-modal sensor inputs (camera, LiDAR, radar, maps) to driving-relevant outputs (trajectories, cost functions, or intermediate representations)\n Train and evaluate models using large-scale datasets from fleet logs, simulation, and synthetic data\n Analyze model performance, identify failure modes, and drive data-driven improvements in robustness and generalization\n Design and refine training pipelines, data workflows, and evaluation strategies to improve iteration speed and model quality\n Contribute to model architecture decisions, including approaches such as imitation learning, reinforcement learning, transformers, and vision-language-action (VLA) models\n Collaborate closely with Perception, Prediction, Planning, and Simulation teams to ensure alignment across the autonomy stack\n Support integration of E2E models into simulation and on-vehicle systems for closed-loop validation\n Improve tooling, experimentation workflows, and reproducibility across the team\n Mentor junior engineers and contribute to team-level best practices and technical discussions\n \n What You’ll Need to Succeed \n \n Bachelor’s degree with 6+ years, Master’s with 3+ years, or PhD with 1+ years of experience in Machine Learning, Robotics, Computer Science, or a related field with a track record of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)\n Experience developing and deploying ML models for autonomous systems, robotics, or complex decision-making environments\n Strong programming skills in Python and PyTorch, with ability to write production-quality ML code\n Experience training and evaluating models using large-scale datasets and distributed compute environments\n Solid understanding of ML architectures used in E2E systems, such as Transformers, BEV models, VLA/VLM approaches, or diffusion models\n Proven ability to debug model behavior, analyze performance metrics, and drive iterative improvements\n Experience contributing to or influencing model architecture and training strategies\n Ability to work cross-functionally and integrate ML systems into larger autonomy pipelines\n \n Bonus Points \n \n Experience developing End-to-End or mid-to-end models for autonomous driving or robotics\n Experience with vision-language models (VLMs) or vision-language-action (VLA) systems\n Familiarity with closed-loop simulation and evaluation frameworks\n Experience with reinforcement learning or imitation learning in real-world systems\n Experience with distributed training frameworks (e.g., Ray)\n Understanding of vehicle dynamics, motion planning, or multi-agent systems\n \n Work Location: For this position, we are open to hiring in Ann Arbor, MI (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States.\n Perks of Being a Full-time Torc’r  \n Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:   \n \n A competitive compensation package that includes a bonus component and stock options\n 100% paid medical, dental, and vision premiums for full-time employees   \n 401K plan with a 6% employer matchFlexibility in schedule and generous paid vacation (available immediately after start date)Company-wide holiday office closures\n AD+D and Life Insurance  \n \n At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, co","salary_min":226400,"salary_max":271700,"location":"Ann Arbor, MI","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","payments","robotics","reinforcement-learning","distributed-systems","autonomous-vehicles","diffusion-models","pytorch"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8518797002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-22T17:31:11Z","expires_at":"2026-09-29T13:36:07.522558Z","created_at":"2026-04-30T05:49:30.050228Z","updated_at":"2026-08-30T13:36:07.656262Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/60e8de00-2b5a-4ee6-bdb3-1037d5268168"},{"id":"031d9dd7-d656-4e6f-b47e-afaa9fbc6057","company_id":"75dcf7c0-5121-45f1-8d1b-6bfbfe15072f","title":"Helix AI Engineer, Generative AI","slug":"helix-ai-engineer-generative-ai-397c6aa2","description":"Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason, and act in the real world. Figure is headquartered in San Jose, CA, and this role requires 5 days/week in-office collaboration.\n Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a Helix AI Engineer, Generative AI to build and scale generative models that enable robots to understand, simulate, and interact with the physical world. This role focuses on training and deploying diffusion and generative models across vision, video, and multimodal domains, with applications spanning perception, data generation, and model-based reasoning.\n Responsibilities \n \n Design, train, and deploy large-scale generative models, with a focus on diffusion-based approaches for vision, video, and multimodal data\n Develop models that improve robot perception, world modeling, and prediction from raw sensory inputs\n Build generative systems for synthetic data creation, augmentation, and dataset scaling for robot learning\n Explore and implement state-of-the-art techniques in diffusion, generative modeling, and multimodal foundation models\n Optimize training pipelines for large-scale generative models across distributed systems\n Work closely with data, training infrastructure, and agent teams to integrate generative models into the full autonomy stack\n Evaluate model quality, robustness, and generalization across real-world scenarios\n Contribute to the design of scalable experimentation frameworks for generative model development\n \n Requirements \n \n Experience training and deploying generative models (diffusion, autoregressive, or related approaches) at scale\n Strong understanding of modern deep learning techniques for vision and/or multimodal systems\n Proficiency in Python and deep learning frameworks such as PyTorch\n Experience working with large-scale datasets and distributed training systems\n Strong experimental rigor and ability to iterate quickly on model performance\n Solid software engineering skills and ability to build reliable, maintainable systems\n Ability to operate independently and own ambiguous, high-impact technical problems\n \n Bonus Qualifications \n \n Experience with diffusion models for image or video generation\n Experience with multimodal foundation models (vision-language or vision-language-action)\n Background in synthetic data generation or simulation for robotics or embodied AI\n Experience optimizing large-scale training (multi-node, GPU clusters, etc.)\n Familiarity with 3D, video prediction, or world models\n Prior work in robotics, embodied AI, or real-world ML systems\n Publication record in machine learning, computer vision, or generative modeling\n \n The US  base  salary range for this full-time position is between $200,000 - $400,000\n The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.","salary_min":200000,"salary_max":400000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","diffusion-models","gpu","robotics","pytorch","computer-vision","generative-ai","deep-learning"],"apply_url":"https://job-boards.greenhouse.io/figureai/jobs/4671699006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-09T15:08:07Z","expires_at":"2026-09-29T13:36:22.917766Z","created_at":"2026-04-13T09:42:03.139006Z","updated_at":"2026-08-30T13:36:23.050916Z","company_name":"Figure AI","company_slug":"figure-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=figure.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/031d9dd7-d656-4e6f-b47e-afaa9fbc6057"},{"id":"d8108d17-7273-4ca4-ac36-9747b09deeac","company_id":"e597788a-bd36-460e-8d1a-40fdbfbcc5c3","title":"World Model Research Scientist- Physical AI","slug":"world-model-research-scientist-physical-ai-e1bfa299","description":"Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.\n Kodiak is building AI that doesn't just perceive the world, it learns how the physics of the world works. We are developing large-scale generative world models that learn to predict realistic, physically consistent futures from real-world sensor data. This capability serves as the foundation for scalable closed-loop training, validation, and long-tail scenario generation, and is distilled into the onboard models that drive our autonomous trucks. We are looking for a research scientist to lead the design and development of world models capable of generating multi-sensor, multi-view, temporally coherent driving scenarios conditioned on actions, 3D scene context, and text. \n  \n In this role, you will: \n \n Design and train generative world models that synthesize realistic multi-camera video and LiDAR conditioned on ego trajectories, 3D scene context, and text \n Research and implement conditional diffusion architectures for driving, including spatiotemporal attention, latent space design, and action-conditioned generation \n Develop techniques for multi-view geometric consistency in generated outputs, drawing on neural rendering, cross-view attention, and 3D-aware generative approaches \n Build methods for joint multimodal generation that maintain cross-sensor consistency between camera, LiDAR, and radar outputs \n Design evaluation frameworks that measure world model quality beyond pixel-level metrics, including scenario fidelity and autoregressive stability \n Scale training pipelines to learn from thousands of hours of real-world driving data across multiple sensor modalities \n \n What you'll bring: \n \n PhD in Computer Science, AI, Robotics, or a related field, with a focus on generative modeling, neural rendering, or video synthesis \n Strong publication record or demonstrated research contributions in diffusion models, video generation, neural radiance fields, 3D-aware generative models, or world models \n Experience with neural rendering and view synthesis and an understanding of multi-view geometric consistency \n Proficiency working with multimodal sensor data (camera, LiDAR, radar) and familiarity with 3D representations such as BEV grids, voxel fields, or tri-planes \n Strong implementation skills in Python and PyTorch, with experience training large generative models at scale using distributed training \n Passion for building AI that understands and predicts the physical world to enable safe autonomous driving \n \n What We Offer: \n \n Competitive compensation package including equity and annual bonuses \n Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits) \n MetLife Legal Services, Identity \u0026 Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, \u0026 Critical Illness Insurance \n Flexible PTO, 10 paid holidays, and generous parental leave policies \n Our office is centrally located in Mountain View, CA \n Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging \n Long Term Disability, Short Term Disability, Life Insurance \n Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation)  \n Fidelity 401(k) \n Commuter, FSA, Dependent Care FSA, HSA \n Various incentive programs (referral bonuses, patent bonuses, etc.) \n The pay range listed below reflects the base salary  in our SF/Silicon Valley location,  across several internal levels. Actual starting pay will be based on job-related factors including: work location, experience, relevant training, education, skill level and performance during interview. Total compensation at Kodiak includes base pay, equity, bonus and a competitive benefits package\n California Pay Range\n $190,000 — $250,000 USD \n  \n At Kodiak, we strive to build a diverse community working towards our common company goals in a safe and collaborative environment where harassment of any kind is strictly prohibited. Kodiak is committed to equal opportunity employment regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or veteran status, or any other basis protected by applicable law.\n  \n In alignment with its business operations, Kodiak adheres to all r","salary_min":190000,"salary_max":250000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","distributed-systems","autonomous-vehicles","computer-graphics","diffusion-models","robotics","data-pipeline","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/kodiak/jobs/4203253009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-27T19:53:56Z","expires_at":"2026-09-29T13:38:44.836085Z","created_at":"2026-04-13T09:41:40.245143Z","updated_at":"2026-08-30T13:38:44.971816Z","company_name":"Kodiak","company_slug":"kodiak","company_logo_url":"https://www.google.com/s2/favicons?domain=kodiak.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d8108d17-7273-4ca4-ac36-9747b09deeac"},{"id":"8b201db2-31c5-468a-bf56-8929044f813d","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Data Scientist","slug":"senior-data-scientist-27f7c40f","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 Rigorous performance evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo’s audacious goals. Waymo data scientists work hand-in-hand with engineering teams at each stage of the software development cycle, employing statistical models and developing metrics and measurement frameworks to ensure that the Waymo Driver meets our strict standards for safety, compliance, and driving and service quality. Autonomous driving presents a new paradigm in data science: in addition to leveraging data collected on-road, we generate our own data using state-of-the-art simulation technology—resulting in denser signals and challenging new problems in estimation and experimental design.\n In this hybrid role you will report to a data science manager.\n You will: \n \n Develop evaluation frameworks for autonomous vehicle performance, for large-scale ML models, and for the quality of simulation.\n Develop new metrics, interpret trends, and investigate anomalies in data from simulation and on-road driving.\n Develop novel statistical methods to handle unique aspects of AV data; e.g. rate estimation with rare events, combining real and synthetic data, etc.\n Frame and solve ambiguous problems by scoping technical priorities and innovating on statistical methods.\n Derive data-driven conclusions and communicate findings to senior stakeholders.\n Establish yourself as the point-of-contact for a significant project area by using data to drive technical decisions and demonstrate success.\n Collaborate with Product and Engineering partners developing the Waymo Driver and Waymo’s simulation software; facilitate deployment readiness decisions for both products.\n Mentor other data scientists and provide constructive technical feedback within the team and across Waymo.\n \n You have: \n \n Degree in a quantitative field (e.g. Statistics, Mathematics, Physics)\n 5+ years of industry experience solving data science problems, or a PhD in a quantitative field and 3+ years of industry experience\n Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models\n Demonstrated knowledge of Python/SQL/R data analysis libraries and packages\n \n We prefer: \n \n PhD in a quantitative field\n A demonstrated track record of independently driving data science projects to deliver business value\n Experience solving problems related to Autonomous Driving or Ride Hailing\n Experience in adjacent relevant areas like Advanced Machine Learning (Deep Learning and Diffusion models), Traffic Modeling, Safety Evaluation or Prediction\n \n   \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","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","diffusion-models","autonomous-vehicles","data-science"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7456042","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-12-17T22:09:14Z","expires_at":"2026-09-29T13:35:05.692136Z","created_at":"2026-05-27T14:04:35.671538Z","updated_at":"2026-08-30T13:35:05.828796Z","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/8b201db2-31c5-468a-bf56-8929044f813d"},{"id":"a7f6955e-818e-46fe-8e78-fa40ec975427","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Data Scientist","slug":"data-scientist-fd3e14a9","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 Rigorous performance evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo’s audacious goals. Waymo data scientists work hand-in-hand with engineering teams at each stage of the software development cycle, employing statistical models and developing metrics and measurement frameworks to ensure that the Waymo Driver meets our strict standards for safety, compliance, and driving and service quality. Autonomous driving presents a new paradigm in data science: in addition to leveraging data collected on-road, we generate our own data using state-of-the-art simulation technology—resulting in denser signals and challenging new problems in estimation and experimental design.\n In this hybrid role you will report to a Data Science Manager.\n You will: \n \n Develop evaluation frameworks for autonomous vehicle performance, for large-scale ML models, and for the quality of simulation.\n Develop new metrics, interpret trends, and investigate anomalies in data from simulation and on-road driving.\n Develop novel statistical methods to handle unique aspects of AV data; e.g. rate estimation with rare events, combining real and synthetic data, etc.\n Frame and solve ambiguous problems, derive data-driven conclusions, and communicate findings to senior stakeholders.\n Collaborate with Product and Engineering partners developing the Waymo Driver and Waymo’s simulation software; facilitate deployment readiness decisions for both products.\n \n You have: \n \n Degree in a quantitative field (e.g. Statistics, Mathematics, Physics)\n 3+ years of industry experience solving data science problems or a PhD in a quantitative field\n Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models\n Demonstrated knowledge of Python/SQL/R data analysis libraries and packages\n \n We prefer: \n \n PhD in a quantitative field\n Experience solving problems related to Autonomous Driving or Ride Hailing\n Experience in adjacent relevant areas like Advanced Machine Learning (Deep Learning and Diffusion models), Traffic Modeling, Safety Evaluation or Prediction\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 $170,000 — $216,000 USD","salary_min":170000,"salary_max":216000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["diffusion-models","autonomous-vehicles","deep-learning","data-science"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7455592","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-12-17T21:52:25Z","expires_at":"2026-09-29T13:35:03.533198Z","created_at":"2026-05-27T14:04:33.984425Z","updated_at":"2026-08-30T13:35:03.67309Z","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/a7f6955e-818e-46fe-8e78-fa40ec975427"},{"id":"4d2c56f0-6125-4fcb-943c-f06dadcac0e7","company_id":"db8b936f-1339-4a06-9eff-39a0930f819d","title":"Research Engineer, Materials Science","slug":"research-engineer-materials-science-e3218292","description":"At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.\n  \n \n \n \n \n Snapshot \n \n   \n \n Science is at the heart of everything we do at Google DeepMind. From the beginning, we took inspiration from science to build better algorithms, and now, we want to use our toolkit to accelerate scientific discovery. By bringing together specialists with backgrounds in machine learning, computer science, physics, chemistry, biology and more, we’re optimistic that we can build new methods that will push the boundaries of what is possible and help solve the biggest problems facing humanity.\n \n \n \n Project Overview\n \n   \n \n  \n Google DeepMind (GDM) is pursuing a ground-breaking research program in materials, aiming to accelerate the discovery of new functional materials by combining the predictive power of artificial intelligence (AI) and computational simulation with automated experimentation.\n You'll join an interdisciplinary team of domain experts, ML researchers, and engineers exploring a diverse set of important scientific problems in materials science, physics, quantum chemistry and other areas. Our work is organised into several longer-term focus areas, which aim to achieve step changes to the state-of-the-art (as exemplified in e.g. DM21 and GNoME ).\n  \n \n \n \n The role\n \n   \n \n To succeed in this role you will need to be passionate about advancing material science using machine learning and other computational techniques.\n As an embedded Research Engineer you will collaborate with other researchers and engineers to develop infrastructure for running experiments and help researchers explore new applications of AI and LLMs to materials science. The team is pioneering in many different domains so you will take part in exploratory work that enables validating early ideas, and work in a maturing area to deepen and build infrastructure to exploit a promising line of research. You will also contribute to the scientific knowledge and experience of the team with your own scientific domain knowledge.\n Key responsibilities: \n \n Plan and perform rapid prototyping of machine learning techniques applied to problems in science.\n Undertake exploratory analysis to inform experimentation and research directions.\n Make improvements to model architectures and training procedures of machine learning models.\n Implement tools, libraries and frameworks to speed up and enable new research.\n Report and present software developments, experimental results and data analysis clearly and efficiently.\n Collaborate with internal and external scientific domain experts.\n \n \n \n \n About you\n \n   \n \n  \n Research Engineers come from a diverse set of backgrounds, sometimes with degrees in Computer Science and sometimes with extensive experience with real problems, or both. \n In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:\n \n Degree in computer science, electrical engineering, science, mathematics or equivalent experience.\n Experience applying software engineering principles in a scientific research environment.\n Knowledge of linear algebra, calculus and statistics equivalent to at least first-year university coursework.\n Experience exploring, analysing, and visualising large and noisy datasets.\n Experience using Jax, PyTorch, TensorFlow, NumPy, Pandas or similar ML/scientific libraries.\n \n \n In addition, we also look for at least one of the following:\n \n Specific domain expertise in areas like inorganic chemistry, solid-state physics, or materials synthesis.\n Experience applying modern deep learning architectures (e.g., transformers, diffusion models) to chemistry or material science challenges (e.g. ML force fields).\n Experience running large-scale scientific simulations (e.g. molecular dynamics, computational chemistry simulations, etc.) on Cloud or HPC clusters.\n Experience developing custom LLM agents or tool-using systems.\n Experience with concurrent and distributed software algorithms and architectures.\n Masters or PhD in computer science, electrical engineering, science, mathematics or equivalent experience.\n \n \n \n   \n   \n \n The US base salary range for this full-time position is between $141,000 - $202,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.\n Note: In the event your application is successful and an offer o","salary_min":141000,"salary_max":202000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["llm","tensorflow","fine-tuning","diffusion-models","deep-learning","search","pytorch","research"],"apply_url":"https://job-boards.greenhouse.io/deepmind/jobs/7339890","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-10-31T19:53:50Z","expires_at":"2026-09-29T13:33:07.062666Z","created_at":"2026-05-28T14:03:14.175221Z","updated_at":"2026-08-30T13:33:07.204089Z","company_name":"DeepMind","company_slug":"deepmind","company_logo_url":"https://www.google.com/s2/favicons?domain=deepmind.google\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4d2c56f0-6125-4fcb-943c-f06dadcac0e7"}],"page":1,"per_page":20,"total":76,"total_is_exact":true,"total_pages":4}