{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL (Reinforcement Learning) ","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["gpu","alignment","search","jax","distributed-systems","code-generation","pytorch","llm"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-08-14T14:00:28.788703Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-07-15T14:00:28.927351Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"f47b2b52-9138-4056-a197-783873a96c39","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Voice Model","slug":"member-of-technical-staff-voice-model-5b5f6cb9","description":"SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge.  Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \n ABOUT THE ROLE:\n You will join the Grok Voice Model team to help build the world’s best voice AI. We deliver smooth, natural, low-latency spoken interactions — expressive, multilingual, and reliable across devices and real-time scenarios. We own the full training pipeline: massive data curation, premium audio processing, frontier speech-language pre-training, and intensive post-training to push quality, speed, and stability to the limit.\n Our goal: make talking to AI feel like conversing with the most charming, kind, and knowledgeable person imaginable. We’re seeking exceptionally smart, execution-oriented engineers to help us get there.\n RESPONSIBILITIES:\n \n Design and execute large-scale speech data curation and processing pipelines, including collection of diverse real-world audio, synthetic data generation, and automated annotation workflows to enable high-quality model training and evaluation.\n Work on pre-training and post-training of speech-language models, with targeted enhancements through supervised fine-tuning, reinforcement learning, and other techniques to ensure Grok Voice responses are accurate, factually grounded, natural and idiomatic in spoken style, conversational in tone, and fluent across multiple languages.\n Build and iterate a comprehensive evaluation framework covering objective metrics (accuracy, quality, latency, expressiveness), human preference studies, content factuality assessments, real-time interaction quality, and experimentation infrastructure to measure and improve performance.\n Work closely with product teams to integrate voice models into applications and real-time environments, define spoken interaction specifications, and handle the full lifecycle from prototype to global-scale deployment for stable, low-latency, delightful voice experiences.\n \n BASIC QUALIFICATIONS:\n \n Python expert with deep proficiency in writing clean, efficient code for AI/ML systems.\n Hands-on experience processing large-scale datasets using tools like Spark and Ray for cleaning, augmentation, and feature extraction.\n Proficiency in pre-training and post-training speech-language models using JAX/PyTorch, including supervised fine-tuning, reinforcement learning, and optimizations for accuracy, factuality, natural spoken style, detail, and multilingual fluency.\n Ability to set up and run rigorous evaluation pipelines: objective metrics, human preference studies, content factuality checks, and iterative A/B testing to drive model improvements.\n Experience building or working with large-scale distributed training and inference systems on Kubernetes.\n Proactive, self-driven attitude — ready to grind in a fast-paced, high-caliber team to deliver outstanding voice AI experiences.\n \n COMPENSATION AND BENEFITS:\n $150,000 - $450,000 USD\n Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n SpaceXAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice .","salary_min":150000,"salary_max":450000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["speech","fine-tuning","reinforcement-learning","distributed-systems","pytorch","pre-training"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5051966007","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-16T20:39:18Z","expires_at":"2026-08-14T14:04:44.897369Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-07-15T14:04:45.027875Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f47b2b52-9138-4056-a197-783873a96c39"},{"id":"9cf703e4-28cb-47a7-9151-d26f9745f43d","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Machine Learning Engineer, Relevance and Personalization (Query Intelligence)","slug":"senior-machine-learning-engineer-relevance-and-personalization-query-intelligence-b6fdeb9a","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n The Community You Will Join: \n The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. In this role you'll focus on query intelligence, the front door of search working on critical, impactful projects that turn what a guest types, taps, or says into a precise understanding of their intent, spanning autocomplete and smart compose, query tagging, query expansion, and intent modeling across Stays, Experiences, and Services.\n The Difference You Will Make: \n Query understanding is where every search begins, and it directly shapes retrieval, ranking, and ultimately the perfect match between guests and hosts. We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) and increasingly large language models at Airbnb. You'll build the models that parse free-form and natural-language multimodal queries, extract entities and location context, classify intent, and anticipate what guests want before they finish typing. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb's mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here: https://sites.google.com/view/airbnb-relevance-publications/home \n A Typical Day:  \n \n Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases, with a focus on query understanding.\n Develop query understanding capabilities — autocomplete and smart compose, query tagging (sequence tagging / NER), query expansion, and query/user intent modeling — and natural-language (\"search in your own words\") search experiences powered by modern NLP and LLMs.\n Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.\n Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.\n Leverage third-party and in-house Machine Learning tools \u0026 infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.\n Example projects include: smart compose and language generation for search, LLM-based sequence taggers, LLM-driven query/location expansion, intent classification, and user-intent sequence modeling.\n \n Your Expertise: \n \n 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.\n Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.\n Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. natural language processing, personalization, search and recommendation, marketplace optimization).\n Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).\n Industry experience building end-to-end Machine Learning models.\n Experience applying large language models and modern NLP — e.g., sequence tagging/NER, text generation, intent classification, or embedding/representation learning.\n Familiarity with building natural-language, AI-native and agentic search experiences is a plus.\n Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your po","salary_min":200000,"salary_max":235000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","generative-ai","data-pipeline","agents","search","nlp","pytorch","tensorflow"],"apply_url":"https://careers.airbnb.com/positions/8065789?gh_jid=8065789","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T23:54:51Z","expires_at":"2026-08-14T14:11:22.875202Z","created_at":"2026-07-15T14:11:23.002744Z","updated_at":"2026-07-15T14:11:23.002744Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9cf703e4-28cb-47a7-9151-d26f9745f43d"},{"id":"021f3b70-f0d5-4666-a5e1-431d120b0e63","company_id":"31ae48bc-c938-4c26-a348-0bf3c089a446","title":"Senior Software Engineer - GPU Kernel Authoring \u0026 Optimization","slug":"senior-software-engineer-gpu-kernel-authoring-optimization-d4eed12b","description":"CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com . \n About the role: \n CoreWeave is the top-rated AI-cloud for high-performance GPU infrastructure across AI/ML, visual effects, rendering, and real-time inference. Our stack is engineered for speed, scale, and cost-efficiency—an unmatched alternative to traditional hyperscalers. At CoreWeave, infrastructure is the product.\n We're looking for a Senior Engineer for CoreWeave's Benchmarking \u0026 Performance team, focused on kernel authoring and optimization. You will write, profile, and tune the GPU kernels that sit on the critical path of large-scale model serving—squeezing maximum throughput and minimum latency out of every SM, tensor core, and byte of memory bandwidth. You will also aid us in achieving industry-leading end-to-end performance benchmarking publications such as MLPerf.\n You will be an owner who leads designs, raises engineering standards, and delivers measurable improvements to latency, throughput, and reliability across our inference stack. You'll partner with product, orchestration, and hardware teams to turn kernel-level wins into end-to-end gains and meet strict P99 SLAs at scale.\n \n Author, profile, and optimize CUDA kernels—GEMMs, attention, MoE routing, quantization, KV-cache, and fused epilogues—on the critical path of LLM inference.\n Optimize for the hardware: exploit tensor cores and tune occupancy, memory coalescing, shared-memory/register usage, and overlap of compute with data movement.\n Use kernel-authoring DSLs and compilers to prototype and ship kernels quickly without sacrificing performance.\n Benchmark rigorously: build reproducible microbenchmarks and roofline analyses, and validate that kernel-level wins translate to end-to-end latency/throughput gains across model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang).\n Implement and maintain benchmarking workflows for end-to-end MLPerf Inference (and Training) runs, including workload setup, cluster configuration, runbooks, and result validation.\n Lead design reviews and drive architecture within the team; decompose multi-service work into clear milestones.\n Mentor junior engineers; review cross-team designs and elevate coding/testing standards.\n Help ensure reproducible, well-documented benchmarking and kernel-optimization processes.\n \n Who You Are: \n \n 5+ years of experience building high-performance computing, GPU/accelerator software, or performance-critical systems.\n Hands-on CUDA experience is required—you have written and optimized custom kernels and are fluent with the CUDA programming and memory model.\n Deep understanding of GPU architecture and performance: tensor cores, warp/occupancy tuning, the memory hierarchy and bandwidth, NVLink/PCIe, and profiling with Nsight Compute/Systems.\n Strong coding in C++ and Python; comfortable reading and writing low-level, performance-sensitive code.\n Familiarity with model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang) and the kernels that dominate their inference cost.\n Strong communicator comfortable collaborating with cross-functional teams and external partners.\n \n Preferred: \n \n Triton or Mojo for authoring custom GPU kernels — highly desired.\n CuTe DSL for Python-based kernel authoring on NVIDIA GPUs.\n JAX and its Pallas kernel language for authoring kernels on GPU/TPU.\n HIP / ROCm and AMD GPU experience.\n NCCL and collective-communication performance.\n Experience with alternative accelerators such as Google TPUs and Meta's MTIA.\n Familiarity with kernel-authoring DSLs and nano-compilers such as KNYFE and its Block DSL.\n Experience with Kubernetes at production scale.\n Experience with SUNK (Slurm on Kubernetes) / Slurm for scheduling large GPU jobs.\n Experience running MLPerf submissions or similar large-scale audited benchmarks.\n Contributions to OSS projects such as vLLM, SGLang, PyTorch, Triton, or CUTLASS.\n \n Wondering if you're a good fit? \n We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match.\n Why CoreWeave? \n Help shape an industry-defining inference platform that enables teams to deploy generative AI and real-time applications at scale. If squeezing every last microsecond out of GPU kernels and delivering reliable model serving excites you, this is the place to build. We're in an exciting stage of hyper-growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly ","salary_min":182000,"salary_max":242000,"location":"Sunnyvale, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","generative-ai","llm","pytorch","computer-graphics","gpu","jax"],"apply_url":"https://coreweave.com/careers/job?4697100006\u0026board=coreweave\u0026gh_jid=4697100006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T22:01:55Z","expires_at":"2026-08-14T14:06:51.780451Z","created_at":"2026-07-15T14:06:51.909822Z","updated_at":"2026-07-15T14:06:51.909822Z","company_name":"CoreWeave","company_slug":"coreweave","company_logo_url":"https://www.google.com/s2/favicons?domain=coreweave.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/021f3b70-f0d5-4666-a5e1-431d120b0e63"},{"id":"d3e4d203-9a98-43d2-b9d4-af63039179a3","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Machine Learning Engineer, Sensor Pipelines","slug":"machine-learning-engineer-sensor-pipelines-d82bbef2","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 Perception team at Waymo builds technology that powers the Waymo Driver. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We conduct research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling software engineers like you to develop multi-modal models and techniques at scale.\n The Sensor Pipelines team applies sensor fusion and ML approaches to address critical challenges in Perception; like detections of Collisions, Antagonistic Behaviors like Vandalism, Sensing Occlusions, etc. Our work involves cutting-edge research (Gen AI) to solve real-world problems and requires close collaboration with onboard teams across Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to develop sophisticated models and techniques at scale.\n This role follows a hybrid work schedule and reports to a Technical Lead Manager.\n You will: \n \n Apply sensor fusion, machine learning techniques to build multi-modal sensor fusion architectures and spatial-temporal representation learners to solve real-world challenges\n Develop and deploy machine learning models, including using Generative Artificial Intelligence (Gen AI) system, and non-ML systems to solve those challenging problems\n Develop data mining, labeling, training and eval pipelines to support the onboard development\n Collaborate and work in partnership with product, infra and research teams across Waymo\n \n You have: \n \n Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience\n 3+ years experience in Machine Learning and/or Computer Vision\n Experience with C++ and Python\n Experience with ML frameworks like PyTorch or JAX\n \n We prefer: \n \n MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline\n Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI\n Github repositories or Tech Blogs of LLMs/ VLMs\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["pytorch","autonomous-vehicles","computer-vision","llm","deep-learning","generative-ai","robotics","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8051390","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:03:11Z","expires_at":"2026-08-14T14:06:24.797122Z","created_at":"2026-07-15T14:06:24.923116Z","updated_at":"2026-07-15T14:06:24.923116Z","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/d3e4d203-9a98-43d2-b9d4-af63039179a3"},{"id":"dbf3ed2d-61a8-46d4-8f99-13cd04d28f0e","company_id":"4c109027-78ec-41cd-b57a-dc58e47d0bd0","title":"Senior Machine Learning Scientist I, Model-Driven Optimization","slug":"senior-machine-learning-scientist-i-model-driven-optimization-eb80e6bd","description":"The Role:  \n Generate:Biomedicines is seeking a creative, rigorous, and execution-oriented machine learning scientist to join our Model-Driven Design team. This role will focus on building the ML methods, data strategies, and closed-loop systems that determine what we design, build, test, and learn from next.\n The Model-Driven Design team works at the interface of machine learning, protein design, engineering, and experimental science. We develop and apply models and quantitative frameworks that help Generate discover and optimize therapeutic proteins. In this role, you will help advance the technical foundation of our lab-in-the-loop protein optimization platform, with a focus on sequential decision-making, experimental design, property modeling, and scalable design systems.\n We are looking for someone who can serve as a technical leader and hands-on individual contributor, driving complex, high-impact work from problem framing through implementation, deployment, and experimental impact. The ideal candidate combines depth in probabilistic machine learning, Bayesian optimization, active learning, or related approaches with the practical judgment and engineering discipline to turn technical ideas into reliable systems that drive impact. You will partner closely with protein designers, wet-lab scientists, ML scientists, and engineers to build durable capabilities that accelerate therapeutic discovery.\n This role is part of a highly collaborative team environment that balances in-person collaboration with hybrid flexibility based out of our Somerville, MA office. \n Here's how you will contribute: \n \n Develop new machine learning methods and systems for lab-in-the-loop protein optimization, including property models and multi-objective optimization strategies for therapeutic protein design.\n Shape data-generation and data-use strategies that make experimental campaigns maximally informative for model improvement, therapeutic optimization, and future design cycles.\n Build and apply LLM-enabled and agentic workflows that help scientists explore design hypotheses, connect models to data and experiments, and accelerate iterative learning.\n Design, implement, test, and maintain production-quality ML models, software components, and data workflows, with attention to reliability, reproducibility, observability, and computational efficiency.\n Partner with ML engineering and software teams to integrate these components into robust, scalable platform capabilities, with clear ownership across team boundaries.\n Collaborate closely with protein designers and wet-lab scientists to ensure models and optimization systems are grounded in experimental reality and deliver measurable impact.\n Identify important technical gaps, develop proposals, define milestones, align stakeholders, and help set technical direction across cross-functional programs.\n Communicate clearly across disciplines and help raise technical standards across ML, engineering, protein design, and experimental teams.\n \n The Ideal Candidate will have: \n \n PhD in machine learning, computational biology, computer science, applied mathematics, engineering, or a related quantitative field.\n Strong practical experience with probabilistic machine learning, Bayesian optimization, active learning, experimental design, or related approaches for sequential decision-making under uncertainty.\n Experience developing machine learning methods or systems for biological, biomedical, or experimental scientific data, with an ability to reason about noisy assays, sparse labels, experimental bias, and data-generation strategy.\n Demonstrated ability to translate ML ideas into systems, tools, or workflows that affect real scientific, experimental, or product decisions.\n Strong Python skills and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.\n Strong systems thinking and ability to design technical interfaces, reason about system tradeoffs, and partner with engineering teams to build scalable, maintainable ML infrastructure.\n Excellent communication skills and ability to bridge ML, engineering, protein design, and experimental stakeholders.\n Pragmatic, collaborative working style, with the ability to bring structure to open-ended problems and balance scientific rigor with execution in fast-moving, cross-functional environments.\n \n Nice to have \n \n Experience in protein design, protein engineering, antibody engineering, biologics discovery, or drug development.\n Experience partnering with experimental teams on design-build-test-learn cycles, high-throughput screening, directed evolution, pooled libraries, or model-guided experimental campaigns.\n Experience with multi-objective optimization, uncertainty calibration, model-guided library design, or experimental campaign planning.\n Experience developing and applying deep learning models, including transformer-based architectures\n Experience building or applying LLM agents, scientific copilots, or agentic syste","salary_min":192000,"salary_max":265000,"location":"Somerville, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","healthcare","code-generation","deep-learning","pytorch","machine-learning"],"apply_url":"https://generatebiomedicines.com/open-positions?gh_jid=4696856006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T19:16:51Z","expires_at":"2026-08-14T14:15:56.335011Z","created_at":"2026-07-15T14:15:56.439634Z","updated_at":"2026-07-15T14:15:56.439634Z","company_name":"Generate Biomedicines","company_slug":"generate-biomedicines","company_logo_url":"https://www.google.com/s2/favicons?domain=generatebiomedicines.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dbf3ed2d-61a8-46d4-8f99-13cd04d28f0e"},{"id":"b5fee987-f2ea-4b80-a04f-395e616158d8","company_id":"c93e0284-9c76-4a85-9905-494865ab9278","title":"AI Systems Performance Engineer - New Graduate","slug":"ai-systems-performance-engineer-new-graduate-e4bfa2f7","description":"The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. \n SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets. \n About The Role \n We are seeking a talented and highly motivated AI Systems Performance Engineer to bring up and optimize state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform.\n You'll work hands-on with advanced AI models — such as DeepSeek, GLM, Kimi, GPT OSS, Llama, Qwen, and other frontier architectures — and learn how modern AI systems achieve high throughput, low latency, and efficient large-scale inference.\n In this role, you'll work at the intersection of machine learning and computer systems, collaborating with engineers across model, compiler, runtime, and hardware teams. This is an ideal opportunity for a new graduate who is passionate about understanding how AI models execute on real hardware and wants to help build the next generation of high-performance AI systems.\n Responsibilities \n \n Bring up cutting-edge foundation models, including LLMs and multimodal models, on the SambaNova platform through the SambaNova software stack.\n Analyze and profile model execution to identify performance bottlenecks across model, compiler, runtime, and hardware layers.\n Optimize AI workloads for throughput, latency, memory efficiency, and scalability.\n Collaborate with machine learning, compiler, runtime, and hardware engineers to develop high-performance AI applications.\n Explore and integrate new techniques in model architecture, quantization, scheduling, caching, and memory optimization.\n Develop tools, benchmarks, and performance analysis methodologies for large-scale AI inference.\n Investigate new model architectures and translate research advances into efficient implementations on production AI systems.\n Contribute ideas for dataflow, scheduling, and system optimizations for both single-node and distributed inference.\n \n Basic Qualifications \n \n Bachelor's or Master's degree in computer science, electrical engineering, computer engineering, or a related technical field (e.g., applied mathematics, physics, or statistics), completed or expected before the start date.\n Strong programming skills in Python, C++, or a similar programming language.\n Solid foundations in algorithms, data structures, computer architecture, operating systems, or parallel computing.\n Familiarity with deep learning and at least one major ML framework, such as PyTorch, TensorFlow, or JAX.\n Strong analytical and problem-solving skills, with an interest in understanding and optimizing system performance.\n Ability and enthusiasm to learn across machine learning, software systems, and hardware.\n \n Preferred Qualifications \n \n Coursework, research, internship, or project experience in machine learning systems, computer architecture, compilers, distributed systems, or high-performance computing.\n Hands-on experience with LLMs, multimodal models, or transformer architectures.\n Familiarity with model inference, KV cache, batching, quantization, or distributed execution.\n Experience with GPU or accelerator programming using CUDA, Triton, OpenCL, or similar technologies.\n Familiarity with frameworks such as vLLM, DeepSpeed, Megatron, or TensorRT.\n Understanding of memory hierarchy, caching, parallelism, or scheduling.\n Experience profiling and optimizing the performance of software or ML workloads.\n Research publications, open-source contributions, programming competitions, or technically challenging personal projects are a plus.\n \n We value strong technical fundamentals, curiosity, and the ability to learn quickly. Prior production experience with large-scale AI systems is not required.\n Base Salary Range:\n Base Pay Range\n $135,000 — $165,000 USD \n Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.  \n EEO Policy SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex ","salary_min":135000,"salary_max":165000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["llm","gpu","distributed-systems","deep-learning","tensorflow","generative-ai","pytorch"],"apply_url":"https://sambanova.ai/sambanova-available-positions/?gh_jid=6115124004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:28:28Z","expires_at":"2026-08-14T14:06:10.228422Z","created_at":"2026-07-15T14:06:10.360035Z","updated_at":"2026-07-15T14:06:10.360035Z","company_name":"SambaNova Systems","company_slug":"sambanova","company_logo_url":"https://www.google.com/s2/favicons?domain=sambanova.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b5fee987-f2ea-4b80-a04f-395e616158d8"},{"id":"e6cf414e-5202-4a66-9735-43bcbeb83352","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Senior Machine Learning Engineer, Ads Content Understanding","slug":"senior-machine-learning-engineer-ads-content-understanding-06e45727","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Reddit has a flexible workforce!  If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence.\n Ads Content Understanding (ACU) owns and produces signals that describe what Reddit content is about, how brand safe and suitable it is, and what users are trying to accomplish in commercial conversations. ACU is responsible for:\n \n The Knowledge Graph (entities, brands, products, and relationships across Reddit and external sources).\n Content taxonomies such as IAB, Shopify Standard Product Taxonomy, IAS, and other commercial taxonomies used for targeting, safety, and marketplace dynamics.\n Opinion mining for ads use cases: sentiment, stance, commercial intent, and other qualitative attributes of conversations.\n Shopping / product understanding: detecting product entities, product categories, and product attributes in organic conversations and aligning them with shopping catalogs.\n Signals and tags registry: a unified, governed catalog of ACU signals that powers retrieval, ranking, safety, and insights across Ads Foundations and partner teams. \n \n We are looking for a Senior Machine Learning Engineer (IC4) who will act as a key contributor to the Content Understanding roadmap for the Monetization org.\n This is not a research scientist or pure DS role; success is defined by robust, shipped systems and monetization impact.  The ideal candidate is a pragmatic engineer with strong software engineering fundamentals and solid ML intuition—not a pure research scientist. This is an Applied MLE role, requiring someone who can evaluate when to leverage hosted LLMs versus custom models, help scale content understanding to new modalities (e.g., video), and drive practical ML solutions that deliver business impact. \n Responsibilities: \n \n Operate across the full ML lifecycle (problem framing, data, modeling, evaluation, deployment, monitoring, and oncall), designing scalable ML pipelines and championing responsible AI (bias, safety, explainability) for ACU’s models and signals in production.\n Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU, design reviews, setting technical standards, and uplifting the team’s modeling and systems craft.\n Develop evaluation systems and quality monitoring systems for content understanding signals, using SOTA LM-judge practices. \n Drive operational excellence for ACU’s ML systems by defining SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost)\n Build and evolve content understanding capabilities for commercial conversations (e.g., reviews vs. recommendations vs. comparisons vs. Q\u0026A; sentiment and stance; product entities and categories) and operationalize them as robust signals that power contextual and shopping ads, auto-targeting, new formats, and insights products.\n Drive LLM and modern ML best practices within ACU: define when to prompt, finetune, or distill; design evaluation and safety harnesses; and lead at least one major distillation effort to replace external APIs with in-house models.\n \n Required Qualifications: \n \n 5+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains, or Ads. \n Demonstrated Senior-level technical leadership: has contributed to architecture decisions, standards, and design reviews in their immediate team\n Strong communication skills, with the ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams, especially in ambiguous, cross-team problem spaces like Seekers/Searchers monetization. \n Some experience building and shipping NLP / Language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models), with clear business outcomes (e.g., CTR/ROAS uplift, safety improvements). Experience with commercial or intent modeling is a strong plus.\n \n Preferred Qualifications: \n \n Practical experience using LLMs in production for labeling, evaluation, or distillation (e.g., LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs.\n Significant experience with PyTorch, TensorFlow, or similar, and production-quality code in Python (and ideally one staticall","salary_min":216700,"salary_max":303400,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["nlp","healthcare","tensorflow","llm","pytorch","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8008648","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:21:59Z","expires_at":"2026-08-14T14:10:37.134107Z","created_at":"2026-07-15T14:10:37.261524Z","updated_at":"2026-07-15T14:10:37.261524Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e6cf414e-5202-4a66-9735-43bcbeb83352"},{"id":"e57cf48d-3756-4016-8e50-400a76bbaa5d","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Computer Vision","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 Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development. Labs works across a broad variety of AI/ML initiatives—including core computer vision, multimodal representation learning, heterogeneous graph neural networks, generative modeling, and recommender systems. This is the group that develops the foundation ML models that fully leverage the tens of billions of Pins and the associated knowledge graph to improve the core product.\n We are currently hiring for the Visual Modeling team in Labs, which develops Pinterest's in-house visual encoder. In this role, you'll work with Pinterest's rich visual-text dataset to train large-scale models from scratch that are continuously shipped to production to power visualization features. You'll build multimodal representations that power applications such as recommender systems, Semantic IDs, and a range of downstream ML models. The visual encoder also produces visual tokens that power our in-house VLM and composed image retrieval models. The core visual pod is a small group (~10 engineers) inside Labs, which allows for deep collaboration. For example, engineers working on multimodal representation also contribute to our internal text-to-image generation Canvas project—collaborating on autoencoder design or on reward function development for RL training.\n  \n What you’ll do: \n \n Prototype state-of-the-art visual encoders that power Pinterest's recommender systems and internal visual language models.\n Experiment with billion-scale datasets and gain hands-on experience with large-scale GPU computing.\n Build flexible visual reasoning tools such as composed image retrieval, promptable detection/segmentation, and instruction-tuned embedding and generative models.\n Read research papers, participate in group discussions, and help brainstorm the company's overall visual generative strategy.\n Help collect relevant visual instruction training data that can be shared across multimodal representation, composed image retrieval, text-to-image generation and visual language modeling.\n Publish and share your work through conferences, paper submissions, and blog posts.\n Mentor junior researchers and research interns within the Pinterest Labs organization.\n  \n \n What we’re looking for: \n \n Research engineers and scientists with experience building and training computer vision models.\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 Publications at top ML conferences\n Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring\n \n  \n Relocation Statement: \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.\n This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.\n \n  \n #LI-REMOTE #LI-AK7\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The posit","salary_min":189308,"salary_max":389753,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["deep-learning","search","generative-ai","code-generation","computer-vision","pytorch","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":"2026-08-14T14:10:33.819803Z","created_at":"2026-07-15T14:10:33.975738Z","updated_at":"2026-07-15T14:10:33.975738Z","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":"390fea98-a9ba-4487-890a-77d135398888","company_id":"19955a21-2cd6-41fd-a4a8-19b7a942ac16","title":"Lead Value Engineer - Life Sciences","slug":"lead-value-engineer-life-sciences-4c32b22b","description":"Celonis is the global leader in Process Intelligence and the pioneer of Process Mining technology. As one of the world’s fastest-growing enterprise SaaS companies, we are changemakers pushing the boundaries of what’s possible. We invest heavily in advanced AI capabilities—specifically our Process Intelligence Graph—to turn data insights into immediate business action. We believe there is a massive opportunity to unlock global productivity and sustainability by placing intelligence at the core of every business process. Join our mission to make processes work for people, companies, and the planet.\n \n Role Description \n As a Lead Value Engineer specializing in the Life Sciences, you are pushing the envelope in solving business-critical problems for the world's largest, most diversified life science organizations. You will be working intimately with this strategic client, understanding their uniquely complex objectives—spanning from logistics to the precision distribution of advanced products—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners (e.g., Microsoft, OpenAI, Databricks)..\n With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the intricate realities of our customers’ supply chain networks and enables them to industrialize AI. This unlocks real ROI on AI deployments at scale, ensuring life-saving products reach patients faster and safer. There is no AI without PI. You will prototype these solutions, demonstrate their value to Chief Supply Chain Officers (CSCOs) and operational leaders, and ensure successful implementation, adoption, and value realization to increase the footprint of Celonis across the life sciences sector.\n Key Responsibilities \n \n \n AI Discovery \u0026 Solutioning: Understand the client's overarching AI strategy and the distinct supply chain challenges across both their MedTech portfolios (e.g., mitigating global raw material shortages, optimizing supply chains, managing inventories, or accelerating quality batch releases). As a Celonis product and life sciences domain expert, translate these complex, multi-tiered logistics requirements into innovative AI solutions that drive measurable impact..\n \n Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds in the supply chain are successfully reached.\n \n Hackathons \u0026 Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex, fragmented supply chain networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points in planning, sourcing, manufacturing, and distribution.\n \n Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., autonomous inventory rebalancing or intelligent shipment exception handling).\n \n Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise ERPs (e.g., SAP), Quality Management Systems (QMS), and strict regulatory frameworks (FDA, EMA, GxP).\n \n Domain \u0026 Industry Leadership: Serve as the internal and external technical subject matter expert for the Life Sciences Supply Chain, scaling knowledge across the organization regarding pharmaceutical manufacturing and logistics processes.\n \n Requirements \n \n \n 8+ years of experience leading technical pre-sales and post-sales engagements specifically within Life Sciences, Pharmaceutical, or MedTech supply chains. This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.\n \n Deep understanding of supply chain business processes native to Life Sciences (such as Sales \u0026 Operations Planning (S\u0026OP), Procure-to-Pay, Track \u0026 Trace, Cold Chain Management, or Quality Control/Batch Release) with the ability to translate high-level business needs into specific AI use cases.\n \n Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent chatbots for supplier collaboration, automated extraction of data from complex customs or quality documents).\n \n Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and techn","salary_min":157000,"salary_max":184000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","generative-ai","fine-tuning","llm","agents","cloud"],"apply_url":"https://job-boards.greenhouse.io/celonis/jobs/7800529003?gh_jid=7800529003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T21:19:52Z","expires_at":"2026-08-14T14:10:27.060437Z","created_at":"2026-07-12T14:07:50.167193Z","updated_at":"2026-07-15T14:10:27.247076Z","company_name":"Celonis","company_slug":"celonis","company_logo_url":"https://www.google.com/s2/favicons?domain=www.celonis.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/390fea98-a9ba-4487-890a-77d135398888"},{"id":"90b670fb-c16e-4406-9cd7-c2e700e6570a","company_id":"c0136eba-1fff-477a-8968-c5435a645cd3","title":"Senior AI Infrastructure Engineer - Model Training","slug":"senior-ai-infrastructure-engineer-model-training-1ae1d299","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's AI is only as good as the speed at which we can train it. Every improvement to our models – from GigaFusionNet to large-scale world models – depends on infrastructure that turns thousands of hours of multimodal driving data into training throughput. We are looking for engineers who make model training fast: streaming massive camera, LiDAR, and radar datasets without stalling a single GPU, sharding data and models efficiently across nodes, and extracting every FLOP from the latest hardware. If you measure your impact in tokens per second and GPU utilization, this role is for you. In this role, you will: \n \n Design high-throughput data loading and streaming systems for multimodal sensor data (camera, LiDAR, radar), including dataset formats, sharding strategies, and prefetching pipelines that keep GPUs saturated \n Build and optimize distributed training infrastructure across multi-node GPU clusters, applying data, tensor, pipeline, and fully sharded (FSDP/ZeRO) parallelism to models that don't fit on a single device \n Maximize utilization of modern accelerators such as NVIDIA B200s through mixed-precision training (BF16/FP8), fused kernels, memory optimization, and communication/computation overlap \n Profile end-to-end training pipelines to find and eliminate bottlenecks across storage, network, CPU preprocessing, and GPU compute \n Develop scalable dataset construction pipelines that convert petabytes of raw driving logs into training-ready, streamable formats \n Partner with ML teams to scale new architectures from prototype to full-cluster training runs efficiently and reliably \n \n What you’ll bring: \n \n BS, MS, or PhD in Computer Science or a related field, and at least 2-3 years of industry experience in ML systems or infrastructure \n Hands-on experience with distributed training frameworks and techniques (PyTorch DDP/FSDP, DeepSpeed, Megatron, NCCL) and a strong grasp of parallelism trade-offs \n Experience building high-performance data pipelines for large-scale training, including streaming dataset formats (WebDataset, MosaicML Streaming/MDS, or similar), sharding, and storage/network-aware loading \n Deep understanding of GPU performance: mixed precision, memory hierarchy, kernel fusion, profiling tools (Nsight, PyTorch Profiler), and interconnects (NVLink, InfiniBand) \n Strong Python skills and proficiency in PyTorch internals; systems-level experience (C++/CUDA/Triton) a plus \n Passion for building the infrastructure that lets AI for the physical world train faster, scale further, and improve continuously \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 — $260,000 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,","salary_min":190000,"salary_max":260000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","robotics","autonomous-vehicles","gpu","data-pipeline","distributed-systems","infrastructure","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/kodiak/jobs/4310775009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-09T16:07:51Z","expires_at":"2026-08-14T14:10:22.491332Z","created_at":"2026-07-10T14:08:04.242712Z","updated_at":"2026-07-15T14:10:22.616943Z","company_name":"Kodiak Robotics","company_slug":"kodiak-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=kodiak.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/90b670fb-c16e-4406-9cd7-c2e700e6570a"},{"id":"8e37b314-f237-44dc-a850-dd58524233c1","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Data Scientist","slug":"staff-data-scientist-9bba8726","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n As a Staff Data Scientist, you’ll lead the design and development of scalable ML systems for use cases such as menu recommendation, demand forecasting, offer targeting, and guest personalization. You will serve as a technical thought partner across teams, set best practices, and influence the roadmap for ML-driven products that support key business outcomes. Your work will directly shape strategic decisions and enhance customer experience at scale.  \n This role is for a current vacancy.\n A day in the life (Responsibilities) \n \n Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.\n Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.\n Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.\n Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.\n Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.\n Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.\n Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.\n \n  \n What you'll need to thrive (Requirements) \n \n 5+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact.\n Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation.\n Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions.\n Experience with distributed data processing and training, real-time inference, and ML Ops frameworks\n Prior experience mentoring other data scientists or acting as a tech lead.\n Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems.\n Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).\n Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD.\n Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock.\n Excellent communication skills and the ability to influence both technical and non-technical stakeholders.\n Strong business acumen with the ability to align technical solutions with company goals.\n \n Bonus ingredients* : \n \n An advanced degree in Computer Science, Statistics, or a related STEM field is preferred.\n Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability.\n Experience fine-tuning LLMs and applying reinforcement learning from human feedback (RLHF) to improve model performance and alignment.\n \n  \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-Remote\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. \n Pay Range \n $127,000 — $203,000 CAD \n How Toast Uses AI in its Hiring Process \n Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focus","salary_min":127000,"salary_max":203000,"location":"Canada","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["tensorflow","reinforcement-learning","deep-learning","mlops","llm","pytorch","fine-tuning","data-science"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8052293","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T20:25:38Z","expires_at":"2026-08-14T14:11:50.57728Z","created_at":"2026-07-09T14:09:45.188959Z","updated_at":"2026-07-15T14:11:50.703686Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8e37b314-f237-44dc-a850-dd58524233c1"},{"id":"f04f6e13-ccf2-458b-8576-e7fa94481050","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Data Scientist","slug":"staff-data-scientist-317fda4d","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n As a Staff Data Scientist, you’ll lead the design and development of scalable ML systems for use cases such as menu recommendation, demand forecasting, offer targeting, and guest personalization. You will serve as a technical thought partner across teams, set best practices, and influence the roadmap for ML-driven products that support key business outcomes. Your work will directly shape strategic decisions and enhance customer experience at scale.\n A day in the life (Responsibilities) \n \n Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.\n Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.\n Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.\n Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.\n Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.\n Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.\n Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.\n \n  \n What you'll need to thrive (Requirements) \n \n 7+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact.\n Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation.\n Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions.\n Experience with distributed data processing and training, real-time inference, and ML Ops frameworks\n Prior experience mentoring other data scientists or acting as a tech lead.\n Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems.\n Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).\n Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD.\n Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock.\n Excellent communication skills and the ability to influence both technical and non-technical stakeholders.\n Strong business acumen with the ability to align technical solutions with company goals.\n Experience building services on top of LLMs in a large scale production environment.\n \n Bonus ingredients* : \n \n An advanced degree in Computer Science, Statistics, or a related STEM field is preferred.\n Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability.\n Experience fine-tuning LLMs and applying reinforcement learning from human feedback (RLHF) to improve model performance and alignment.\n \n  \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-Remote\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. You can learn more about how we align pay with local labor markets in our Geographic Pay Zone Philosophy . \n Zone A\n $170,000 — $272,000 USD \n Zone B\n $148,000 — $237,000 USD \n Zone C\n $133,000 — $213,000 USD \n How Toast Uses AI in its Hiring Process \n Throughout ","salary_min":133000,"salary_max":213000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","tensorflow","reinforcement-learning","fine-tuning","deep-learning","mlops","llm","data-science"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8029049","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T20:23:10Z","expires_at":"2026-08-14T14:11:50.505714Z","created_at":"2026-07-09T14:09:45.268862Z","updated_at":"2026-07-15T14:11:50.63104Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f04f6e13-ccf2-458b-8576-e7fa94481050"},{"id":"53df0b7f-aa7f-4625-898a-170692e922fd","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Data Scientist II","slug":"data-scientist-ii-95683f8f","description":"Toast is driven by building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love.\n Toast is revolutionizing the way the restaurant industry does business by pairing technology with an extraordinary commitment to customer success. We help restaurants streamline operations, increase revenue, and deliver amazing guest experiences through our platform that combines restaurant point of sale, guest-facing technology, and award-winning customer support. Join us as we empower the restaurant community to delight guests, do what they love, and thrive.  This role is for a current vacancy.\n Bready* to make a change? \n The Toast AI Engineering team is seeking a Data Scientist to embed data science capabilities into the Toast platform by partnering with engineers and product managers to develop statistical and machine learning models that power key product lines.\n About this Roll* (Responsibilities) : \n \n Apply a diverse set of expertise including data mining, statistical analysis and machine learning to deliver impactful, objective, and actionable data insights that enable informed business and product decisions\n Collaborate with cross-functional teams, including sales, marketing, and product, to identify business opportunities and develop data-driven solutions that drive growth and engagement.\n Partner with line of business teams and collaborate with product managers, engineers and data scientists to foster data-driven decisions that yield significant impacts \n Able to effectively communicate analysis, insights and recommendations to high-level business partners in verbal, visual and written formats\n Thrive in a dynamic and rapidly evolving environment\n \n Do you have the right ingredients* (Requirements) ? \n \n Bachelors in computer science, engineering, math, statistics, economics, or other quantitative discipline; Masters preferred.\n 2+ years of data science experience in an industry environment.\n Have solid statistical and machine learning foundations. Familiar with machine learning concepts (e.g. regression/classification, clustering, offline/online model evaluation). \n Experience with advanced machine learning techniques, including supervised and unsupervised learning, graph algorithms, deep learning (e.g., NLP), recommendation systems, and generative AI.\n Experience with Python and SQL, and ML frameworks (e.g. scikit-learn, Tensorflow, PyTorch)\n Experience with cloud solutions, preferably with AWS tooling (e.g. SageMaker, DynamoDB, Athena, Glue, etc.)\n Experience with model workflow orchestration tool (e.g. Airflow)\n Experience collaborating with engineers, product managers, and other cross-functional teams\n Excellent verbal and written communication skills\n Ability to communicate sophisticated quantitative analysis in a clear, precise, and actionable manner.\n \n Special Sauce* (Nice to Haves):  \n \n Experience working on LLM applications, including prompting, RAG, and evaluation.\n Experience in software engineering best practices and tools including object-oriented programming, test-driven development, CI/CD, git, shell scripting, task orchestration.\n Experience shipping machine learning systems in production environments.\n Experience in A/B testing and other experimentation methodologies for effective product launch measurement.\n \n  \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. \n Pay Range \n $110,000 — $136,000 CAD \n How Toast Uses AI in its Hiring Process \n Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focused on your conversation. All hiring decisions are","salary_min":110000,"salary_max":136000,"location":"Canada","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["generative-ai","tensorflow","deep-learning","cloud","llm","nlp","pytorch","data-science"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8052241","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T20:16:37Z","expires_at":"2026-08-14T14:11:49.3663Z","created_at":"2026-07-09T14:09:43.741168Z","updated_at":"2026-07-15T14:11:49.491623Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/53df0b7f-aa7f-4625-898a-170692e922fd"},{"id":"f1bf694f-6890-4864-b7de-7d77bfbc9a49","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Member of Technical Staff - GPU Infrastructure","slug":"member-of-technical-staff-gpu-infrastructure-63e3e8cc","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\nCore Technical Responsibilities\n\nThis customer-facing role combines deep technical expertise with hands-on implementation. You'll be instrumental in:\n\nCustomer Architecture \u0026 Design\n\n - Partner with clients to understand workload requirements and design optimal GPU cluster architectures\n\n - Create technical proposals and capacity planning for clusters ranging from 100 to 10,000+ GPUs\n\n - Develop deployment strategies for LLM training, inference, and HPC workloads\n\n - Present architectural recommendations to technical and executive stakeholders\n\nInfrastructure Deployment \u0026 Optimization\n\n - Deploy and configure orchestration systems including SLURM and Kubernetes for distributed workloads\n\n - Implement high-performance networking with InfiniBand, RoCE, and NVLink interconnects\n\n - Optimize GPU utilization, memory management, and inter-node communication\n\n - Configure parallel filesystems (Lustre, BeeGFS, GPFS) for optimal I/O performance\n\n - Tune system performance from kernel parameters to CUDA configurations\n\nProduction Operations \u0026 Support\n\n - Serve as primary technical escalation point for customer infrastructure issues\n\n - Diagnose and resolve complex problems across the full stack - hardware, drivers, networking, and software\n\n - Implement monitoring, alerting, and automated remediation systems\n\n - Provide 24/7 on-call support for critical customer deployments\n\n - Create runbooks and documentation for customer operations teams\n\nTechnical Requirements\n\nRequired Experience\n\n - 3+ years hands-on experience with GPU clusters and HPC environments\n\n - Deep expertise with SLURM and Kubernetes in production GPU settings\n\n - Proven experience with InfiniBand configuration and troubleshooting\n\n - Strong understanding of NVIDIA GPU architecture, CUDA ecosystem, and driver stack\n\n - Experience with infrastructure automation tools (Ansible, Terraform)\n\n - Proficiency in Python, Bash, and systems programming\n\n - Track record of customer-facing technical leadership\n\nInfrastructure Skills\n\n - NVIDIA driver installation and troubleshooting (CUDA, Fabric Manager, DCGM)\n\n - Container runtime configuration for GPUs (Docker, Containerd, Enroot)\n\n - Linux kernel tuning and performance optimization\n\n - Network topology design for AI workloads\n\n - Power and cooling requirements for high-density GPU deployments\n\nNice to Have\n\n - Experience with 1000+ GPU deployments\n\n - NVIDIA DGX, HGX, or SuperPOD certification\n\n - Distributed training frameworks (PyTorch FSDP, DeepSpeed, Megatron-LM)\n\n - ML framework optimization and profiling\n\n - Experience with AMD MI300 or Intel Gaudi accelerators\n\n - Contributions to open-source HPC/AI infrastructure projects\n\nGrowth Opportunity\n\nYou'll work directly with customers pushing the boundaries of AI, from startups training foundation models to enterprises deploying massive inference infrastructure. You'll collaborate with our world-class engineering team while having direct impact on systems powering the next generation of AI breakthroughs.\n\nWe value expertise and customer obsession - if you're passionate about building reliable, high-performance GPU infrastructure and have a track record of successful large-scale deployments, we want to talk to you.\n\nApply now and join us in our mission to democratize access to planetary scale computing.\n\nCompensation\n\nCash Compensation Range of $150-300k plus Equity Incentives","salary_min":150000,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","agents","llm","pytorch","generative-ai","gpu","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/297d925e-5a42-40bd-b02f-5c928d226f18/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:45:18.934Z","expires_at":"2026-08-14T14:12:06.855135Z","created_at":"2026-04-13T15:01:32.586506Z","updated_at":"2026-07-15T14:12:06.981969Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f1bf694f-6890-4864-b7de-7d77bfbc9a49"},{"id":"537b089a-1139-46c6-9166-2dc6b9693a2f","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Research Engineer - RL Infrastructure ","slug":"research-engineer-rl-infrastructure-af69c92c","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\nWe train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\n\nWHAT YOU’LL WORK ON\n\n - Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/prime-rl framework.\n\n - Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.\n\n - Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.\n\n - Work on distributed training systems spanning data, tensor, and pipeline parallel workloads.\n\n - Help shape the architecture of our RL training stack, including async rollout and post-training systems.\n\n - Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.\n\n - Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.\n\n - Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.\n\n\n\n\n\nYOU MAY BE A FIT IF YOU HAVE\n\n - Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.\n\n - Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.\n\n - Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.\n\n - Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.\n\n - Strong understanding of GPU architecture, profiling, and performance debugging.\n\n - Ability to identify bottlenecks across the stack and drive improvements from first principles.\n\n - Comfort working in a fast-moving environment with ambiguous problems and high ownership.\n\n\n\n\nESPECIALLY EXCITING\n\n - Experience writing or optimizing CUDA / Triton kernels.\n\n - Experience with compiler or runtime optimization for ML systems.\n\n - Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.\n\n - Experience with multi-node GPU clusters and high-performance networking.\n\n - Contributions to open-source ML systems or infrastructure projects.\n\n - Interest in publishing technical work or sharing insights through engineering blogs and technical writing.\n\n\n\n\nWHY THIS ROLE MATTERS\n\nThe next frontier in AI will not be unlocked by models alone. It will be unlocked by systems that let those models train faster, adapt continuously, and operate across real environments at scale.\n\nThat infrastructure does not exist yet in the form the world needs.\n\nWe’re building it.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n - Cash Compensation Range of $150-350k, plus equity.\n\n - Flexible work arrangements, with the option to work remotely or in person from our San Francisco office.\n\n - Visa sponsorship and relocation support for international candidates.\n\n - Quarterly team offsites, hackathons, conferences, and learning opportunities.\n\n - A deeply technical, high-agency team working on infrastructure for open superintelligence.\n\nIf you’re excited about building the systems foundation for frontier-scale RL an","salary_min":150000,"salary_max":350000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["pytorch","gpu","search","distributed-systems","agents","llm","infrastructure","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/05e4b76b-2570-4c89-baf2-9833fff7378f/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:43:53.584Z","expires_at":"2026-08-14T14:12:07.380628Z","created_at":"2026-04-13T15:01:32.609376Z","updated_at":"2026-07-15T14:12:07.509749Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/537b089a-1139-46c6-9166-2dc6b9693a2f"},{"id":"8c402485-1400-4e3b-aacf-eaa1ab3b5dfb","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Research Engineer - Distributed Training","slug":"research-engineer-distributed-training-19cda6e4","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\nWe train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Semianalysis, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\nWHAT YOU’LL WORK ON\n\n - Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/prime-rl framework.\n\n - Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.\n\n - Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.\n\n - Work on distributed training systems spanning data, tensor, and pipeline parallel workloads.\n\n - Help shape the architecture of our RL training stack, including async rollout and post-training systems.\n\n - Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.\n\n - Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.\n\n - Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.\n\n\n\n\n\nYOU MAY BE A FIT IF YOU HAVE\n\n - Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.\n\n - Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.\n\n - Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.\n\n - Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.\n\n - Strong understanding of GPU architecture, profiling, and performance debugging.\n\n - Ability to identify bottlenecks across the stack and drive improvements from first principles.\n\n - Comfort working in a fast-moving environment with ambiguous problems and high ownership.\n\n\n\n\nESPECIALLY EXCITING\n\n - Experience writing or optimizing CUDA / Triton kernels.\n\n - Experience with compiler or runtime optimization for ML systems.\n\n - Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.\n\n - Experience with multi-node GPU clusters and high-performance networking.\n\n - Contributions to open-source ML systems or infrastructure projects.\n\n - Interest in publishing technical work or sharing insights through engineering blogs and technical writing.\n\n\n\n\n\n\n\nBENEFITS \u0026 PERKS\n\n - Cash Compensation Range of $150-350k, plus equity incentives, aligning your success with the growth and impact of Prime Intellect.\n\n - Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.\n\n - Visa sponsorship and relocation assistance for international candidates.\n\n - Quarterly team off-sites, hackathons, conferences and learning opportunities.\n\n - Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.\n\nIf you’re excited about building the systems foundation for frontier-scale training and open superintelligence, we’d love to hear from you.","salary_min":150000,"salary_max":350000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["llm","pytorch","distributed-systems","pre-training","agents","gpu","search","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/8bd52610-175c-42a7-a7cd-b29c45f9d305/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:43:34.749Z","expires_at":"2026-08-14T14:12:06.052408Z","created_at":"2026-04-13T15:01:32.550978Z","updated_at":"2026-07-15T14:12:06.185751Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8c402485-1400-4e3b-aacf-eaa1ab3b5dfb"},{"id":"ea933b5d-c732-402c-bd2a-5aff99b45796","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior, ML Engineer - VLM","slug":"senior-ml-engineer-vlm-fc80da79","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.   \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   \n Torc is marching toward its AV 3.0 strategy, where end-to-end Vision-Language-Action (VLA) models perceive, reason, and act directly from sensor data. High-quality, semantically rich training data is the single biggest lever for that strategy, and this team owns it.   \n Sitting within Offline Perception, this team turns petabytes of logged multi-modal fleet data (images, kinematics) into VLM/VLA-ready datasets: geometric annotations, scenario-level semantic descriptions, action- and trajectory-grounded labels, and reasoning traces that explain why a maneuver was taken. We run a continuous data flywheel — mine long-tail and failure cases, auto-label at scale, validate quality, and feed curated datasets directly into Torc’s end-to-end VLM/VLA model development. You will own the dataset layer that those models learn from.   \n What You’ll Do   \n \n Own the offline dataset pipeline — design, implement, test, and deploy Cloud-based pipelines that convert logged multi-sensor data into VLM/VLA training datasets, spanning geometric labels (3D/2D detection, tracking, segmentation, depth) through semantic, scenario-level, and action/trajectory-grounded annotations. \n Build VLM-assisted auto-labeling — develop open-vocabulary detection, dense captioning, semantic enrichment, and scene/scenario description generation that move beyond closed-set bounding boxes, using foundation models to scale annotation and cut manual labeling cost. \n Generate reasoning-grounded labels — produce language-grounded reasoning and chain-of-causation style annotations, temporally aligned to ego-motion and trajectories, to support VLA training and explainable driving behavior. \n Mine and curate the long tail — surface rare, difficult, and high-uncertainty scenarios, and build curated datasets that measurably improve downstream VLM/VLA model metrics rather than simply adding volume. \n Close the data flywheel — define dataset schemas, quality metrics, and validation; track auto-labeling quality against model requirements; route model failures back into re-labeling and retraining loops. \n Partner with the end-to-end model team — co-define dataset specifications with VLM/VLA model developers, own the quality bar and delivery cadence, and operationalize a continuous dataset delivery loop into their training pipelines. \n Scale on cloud infrastructure — build distributed, reproducible pipelines using columnar data formats and distributed compute, with disciplined software practices, version control, and documentation. \n Lead and mentor — serve as project lead, guide less-experienced engineers, run design reviews, set coding and annotation standards, and drive alignment across team interfaces to the rest of the organization. \n Stay current — track the latest advances in multimodal models, auto-labeling, and end-to-end autonomous driving, and translate relevant research into production data systems.   \n \n What You’ll Need to Succeed:   \n \n Considered highly skilled and proficient in discipline ; conducts complex, important work under minimal supervision and with wide latitude for independent judgment. \n Scope of Influence: Expected to drive alignment across team interfaces to the rest of the organization. Designs, maintains, and owns team technical solutions and drives consensus. Mentors and guides engineers within the group. \n Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering, or related technical field plus competences typically acquired through 6+ years of experience; OR Master’s Degree in a related technical field plus competences typically acquired through 3+ years of experience.   \n \n Required Qualifications (some combination of the following skills):   \n \n Computer Vision \u0026 Deep Learning — model training and at least two of: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, BEV, Depth Estimation. \n Multimodal / VLM experience — hands-on work with vision-language models, open-vocabulary or zero-shot recognition, dense captioning, or semantic embeddings / search applied to perception data. \n Model Data Curation — building targeted datasets that measurably improve downstream model performance; large-scale Parquet data processing (Databricks, Daft, Pandas, etc.). \n Distributed ML \u0026 data frameworks — PyTorch, Lightning, Ray, Spark, o","salary_min":177300,"salary_max":212800,"location":"Ann Arbor, MI","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["payments","llm","generative-ai","robotics","search","autonomous-vehicles","cloud","pytorch"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8572505002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T13:00:15Z","expires_at":"2026-08-14T14:07:36.002659Z","created_at":"2026-07-09T14:05:54.692584Z","updated_at":"2026-07-15T14:07:36.150578Z","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/ea933b5d-c732-402c-bd2a-5aff99b45796"},{"id":"d6456870-ff5c-4c3f-89d2-a6e8784670b8","company_id":"57a9b50d-a69a-4f6f-9acb-910495c3c359","title":"MTS, Research Engineer","slug":"mts-research-engineer-69babe33","description":"About Us: \n At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n About the Role \n We are looking for a Research Engineer to join our team, operating at the critical intersection of model research and training infrastructure.\n In this role, your time will be split between tackling open-ended research problems—such as designing novel architectures and improving algorithmic efficiency — and building the distributed training systems required to make those research breakthroughs a reality. You won't just be handed a paper to implement; you will be expected to reproduce state-of-the-art results from the literature, identify their limitations, and build the infrastructure needed to push beyond them.\n The most significant advances in deep learning require massive scale. We need engineers who are as comfortable reasoning about gradient descent and loss landscapes as they are about distributed systems, GPU cluster utilization, and data pipelines.\n  \n What You'll Do \n \n Conduct Open-Ended Research: Explore new model architectures, training objectives, and optimization techniques. Formulate hypotheses, design experiments, and iterate quickly based on empirical results.\n Reproduce and Extend State-of-the-Art: Implement and reproduce results from recent machine learning papers. Identify bottlenecks, propose improvements, and scale these methods to larger datasets and models.\n Build and Scale Training Infrastructure: Design, implement, and maintain high-performance, distributed machine learning systems. Optimize training loops, data loaders, and communication overhead across large GPU clusters.\n Bridge Science and Engineering: Translate abstract mathematical concepts and research ideas into robust, bug-free, and efficient code.\n Collaborate Cross-Functionally: Work closely with Research Scientists to unblock their experiments by providing tooling, optimizing code, and co-designing experiments that are hardware-aware.\n \n We Expect You To Have: \n \n Strong programming skills (Python, C++, or Rust) and a commitment to writing clean, maintainable code.\n Deep practical knowledge of machine learning frameworks (PyTorch, JAX, or TensorFlow).\n Experience working with large distributed systems and parallel computing (e.g., CUDA, NCCL, MPI).\n A strong foundation in linear algebra, calculus, probability, and statistics.\n A proven track record of implementing complex deep learning algorithms from scratch.\n \n Nice to Have: \n \n A Master’s or PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related field (or equivalent industry experience).\n Experience with low-level GPU programming (CUDA/Triton) or hardware co-design.\n Familiarity with the challenges of training Large Language Models (LLMs)\n Familiarity with the challenges of inference, and OSS inference engines such as SGLang and vLLM\n Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.\n Base Pay Range (Plus Equity)\n $250,000 — $400,000 USD \n Why Fireworks AI? \n \n Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.\n Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.\n Ownership \u0026 Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.\n Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.\n \n Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.","salary_min":250000,"salary_max":400000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["gpu","search","deep-learning","tensorflow","distributed-systems","generative-ai","pytorch","data-pipeline"],"apply_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4308305009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T01:55:35Z","expires_at":"2026-08-14T14:02:25.80437Z","created_at":"2026-07-09T14:02:13.613892Z","updated_at":"2026-07-15T14:02:25.938275Z","company_name":"Fireworks AI","company_slug":"fireworks-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=fireworks.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d6456870-ff5c-4c3f-89d2-a6e8784670b8"},{"id":"4ee45bf1-b8e9-4cdd-a230-51d0059bd127","company_id":"ccb23d77-c69e-462b-a941-02ce99527e78","title":"Senior Software Engineer, Perception","slug":"senior-software-engineer-perception-cb375ed5","description":"Who we are \n Aurora’s mission is to deliver the benefits of self-driving technology safely, quickly, and broadly.\n The Aurora Driver  will create a new era in mobility and logistics, one that will bring a safer, more efficient, and more accessible future to everyone.\n  \n At Aurora, you will tackle massively complex problems alongside other passionate, intelligent individuals, growing as an expert while expanding your knowledge. For the latest news from Aurora, visit  aurora.tech  or follow us on  LinkedIn .\n  \n Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We’re searching for a Senior Software Engineer, Perception to join our autonomous driving team. In this role, you will collaborate closely with cross-functional engineering teams to design and deploy state-of-the-art machine learning models directly onto our real-time vehicle platform. This is an exciting opportunity to solve complex, high-impact autonomy challenges and directly shape the future of the Aurora Driver.\n In this role, you will \n \n Develop and Optimize Core Perception Solutions: Research and develop state-of-the-art deep learning and machine learning models to improve perception under challenging scenarios, such as long-range multi-sensor detection and degraded sensor conditions.\n Tackle End-to-End Autonomy Challenges: Address object detection, tracking of traffic actors, action recognition, and semantic understanding of diverse traffic scenes.\n Deploy Production-Ready Software: Guide software development from initial prototype to production deployment on a real-time AV platform, leveraging large-scale data sets for training and analysis.\n Collaborate and Iterate: Partner with team members to diagnose, analyze, and resolve failure modes encountered during on-road and simulated testing to produce robust, well-tested systems.\n Scale ML Engineering Pipelines: Build and scale robust ML pipelines to facilitate quick experimentation as well as large-scale training and testing.\n \n   \n Required Qualifications \n \n BS, MS, or PhD in Computer Science, Robotics, Engineering, or a related field with a strong foundation in one or more focus areas of ML, including deep learning, computer vision, recursive state estimation, structured prediction, and optimization.\n 6+ years of research-based or professional experience with C++ and Python .\n Comprehensive grasp of linear algebra, discrete/continuous optimization, supervised/unsupervised methods, and generative/discriminative models.\n Strong publication record at top-tier robotics/computer vision conferences/journals, or significant industry experience in relevant fields (robotics, computer vision, self-driving technology)\n \n   \n Desirable Qualifications \n \n Experience utilizing deep learning frameworks such as PyTorch or TensorFlow .\n Advanced production-level knowledge of C++ is highly preferred.\n Prior experience applying computer vision and machine learning directly to complex robotics problems.\n Experience deploying computer vision/ML models at scale across large physical fleets.\n Proven ability to work effectively in environments requiring close cross-team collaboration.\n Experience working with 3D data, including 3D object detection, tracking, semantic segmentation, or processing point clouds from LiDAR, Radar, and multi-camera system\n \n  \n The base Salary range for this position is $162,000 - $260,000.  Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits. \n  #LI-td-1 #Mid-Senior \n Working at Aurora At Aurora, we bring together extraordinarily talented and experienced people united by the strength of our values. We operate with integrity, set outrageous goals, and build a culture where we win together — all without any jerks.\n We believe in-person work increases collaboration, empathy and our ability to lead effectively. As a result, we operate in a hybrid work environment where Aurorans are in office at least 3 days per week.\n Our Careers page provides insight into what it is like to work at Aurora, and you can find all the latest updates in our Newsroom .\n Our commitment to safety \n At the core of everything we do is our commitment to safety. Building best-in-class self-driving technology will take time, and we believe that each employee at Aurora has a role in contributing to safety, every step of the way. Aurora expects commitment to our safety policies from every employee, and seeks candidates who take an active responsibili","salary_min":162000,"salary_max":260000,"location":"Mountain View, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["computer-vision","autonomous-vehicles","pytorch","deep-learning","robotics","tensorflow"],"apply_url":"https://aurora.tech/jobs/8604304002?gh_jid=8604304002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T23:03:08Z","expires_at":"2026-08-14T14:06:40.624903Z","created_at":"2026-07-09T14:04:51.543619Z","updated_at":"2026-07-15T14:06:40.757913Z","company_name":"Aurora Innovation","company_slug":"aurora-innovation","company_logo_url":"https://www.google.com/s2/favicons?domain=aurora.tech\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4ee45bf1-b8e9-4cdd-a230-51d0059bd127"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":1010,"total_pages":51}