{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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Computational biology is key to elucidating the relationship between these phenotypes and human disease and translating them into actionable outcomes.\n\nWe are looking for a computational biologist with expertise across diverse data modalities, including deep experience in either omics or imaging readouts, a strong understanding of cell and disease biology, and fluency with state-of-the-art analysis techniques. Your expertise will help the team navigate the complexities of identifying therapeutic targets from diverse data, elucidating biological mechanisms, and championing a culture of statistical rigor and experimental design to ensure our analyses meet the highest scientific standards.\n\nIn this role, you will directly impact target prioritization and drug development efforts, advance our understanding of diseases, and aid the development of new treatments. You will be part of a cross-functional team of life scientists, data scientists, bioengineers, software engineers, and machine learning scientists who strive to identify therapeutic targets and develop drugs of high efficacy and low toxicity. Based in South San Francisco, this position reports directly to the Head of Computational Biology and ML-Omics and offers an in-person hybrid schedule of three days per week.\n\nYou will be joining a vibrant biotech startup with many opportunities for significant impact. You will work closely with a highly talented team, learn a broad range of skills, and help shape insitro's culture, strategic direction, and outcomes. Join us, and help make a difference to patients!\n\n \n\n\nRESPONSIBILITIES\n\nMultimodal Analysis \u0026 Target Discovery\n\n - Synthesize Multimodal Insights: Draw insights from multimodal analyses (microscopy, spatial proteomics, bulk/single-cell RNA-seq, human cohort data) to uncover disease mechanisms and generate therapeutic hypotheses\n\n - Identify Therapeutic Targets: Analyze diverse data from disease-relevant in vitro models to identify potential therapeutic targets from perturbation screens\n\n - Discover Biomarkers: Analyze data from diverse sources to identify and validate potential biomarkers for monitoring disease status and progression\n\nExperimental Partnership\n\n - Partner on Experimental Design: Work with experimental biologists to design, troubleshoot, and optimize experiments that generate and validate mechanistic and therapeutic hypotheses\n\n - Provide Domain Expertise: Bring statistical and computational expertise to guide assay development and the biological interpretation of results\n\nAnalytical Rigor \u0026 Communication\n\n - Benchmark and Calibrate: Calibrate analysis tools and workflows, define performance metrics, and conduct benchmarking to select fit-for-purpose solutions\n\n - Communicate Findings: Share results with cross-functional stakeholders through reports, visualizations, presentations, and publications\n\n \n\n\nABOUT YOU\n\nExperience \u0026 Qualifications\n\n - Education \u0026 Tenure: Ph.D. in computational biology, systems biology, bioengineering, computer science, machine learning, or a related discipline, with 3+ years of working experience post-graduation\n\n - Data Modality Depth: Hands-on experience with diverse data modalities, including at least one of the following: single-cell RNA-seq, fluorescence microscopy, spatial proteomics or transcriptomics, or label-free microscopy\n\n - Statistical Foundation: Deep understanding of statistical modeling and data analysis, with a demonstrated ability to rigorously interpret complex datasets and generate mechanistic hypotheses\n\n - Biological Grounding: An understanding of molecular biology or disease biology (e.g., neurological, cardiovascular, or metabolic disorders)\n\n - Programming Skills: Strong programming ability and proficiency with Python scientific packages such as NumPy and pandas\n\n - Publication Record: Meaningful contributions to high-quality work published in relevant computational biology, systems biology, life sciences, or biomedical venues\n\nCore Competencies\n\n - Collaborative Communicator: You communicate effectively and collaborate well with people of diverse backgrounds and job functions\n\n - Engineering Discipline: You write well-commented code and documentation and are familiar with coding best practices such as version control and code review\n\n \n\n\nCOMPENSATION \u0026 BENEFITS AT INSITRO\n\nOur target starting salary for successful US-based applicants for this role is $183,000 - $194,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.\n\nThis role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) an","salary_min":183000,"salary_max":194000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["healthcare","payments","machine-learning","data-science"],"apply_url":"https://jobs.ashbyhq.com/insitro/ec4a278a-dd78-4e9c-a203-87e3e2f59e60/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T21:54:47.039Z","expires_at":"2026-09-29T13:36:50.193262Z","created_at":"2026-08-29T13:37:15.708222Z","updated_at":"2026-08-30T13:36:50.329078Z","company_name":"Insitro","company_slug":"insitro","company_logo_url":"https://www.google.com/s2/favicons?domain=insitro.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6e9d10aa-49d2-4e23-ba39-8a7c0475817b"},{"id":"e82c02c8-1f82-44e8-a6a9-52c07aaa1af9","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Machine Learning Engineer II, Responsible AI","slug":"machine-learning-engineer-ii-responsible-ai-748de33f","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 The Responsible AI team is part of the Advanced Technologies Group (ATG), Pinterest’s advanced machine learning team. ATG’s goal is to keep Pinterest at the forefront of machine learning technology across multiple use cases including recommendations, ranking, content understanding, and more. It is an applied team that works horizontally across the company on state of the art AI and ML and works on directly bringing that technology to the product in collaboration with product engineering teams. The team also publishes its work in applied research conferences, but the main goal of the team is to have a direct impact on business metrics.\n At Pinterest our goal is to inspire pinners (our users) to live the life they love. The product is powered by state of the art ML algorithms which are used to understand both the billions of visually rich items on the platform and the interests of our 535M+ monthly active users and recommend inspiring personalized content to them. What this means is that ML at Pinterest is not only multi-modal utilizing image, text, and graph signals as input, but it needs to operate at a very large scale and often in a real time interactive user experience.\n Pinterest is known for being a positive and inspirational place on the internet and we believe that inspiration begins with representation and belonging. The Responsible AI team is a horizontal team that collaborates across the company on various initiatives. These range from Generative AI alignment, evaluation, and mitigations - ensuring these models are safe, bias-free, and aligned with Pinterest’s vision and policies - to championing ML Fairness and developing user-facing features that enhance our product for all users. This includes launching groundbreaking features such as user-controllable skin tone , hair pattern search refinements, and more recently, body type .\n You’ll help shape forward-thinking projects, develop advanced ML approaches rooted in fairness and equitability, and pioneer responsible AI safeguards for emerging technologies. By joining this team, you’ll make a lasting impact on our Pinners, our business, and the evolution of ethical AI at Pinterest.\n What you'll do: \n \n execute on projects in the responsible AI frontier, to identify, avoid, and mitigate bias across a wide range of ML applications at Pinterest including generative AI. \n Collaborate with other engineering teams (trust and safety, user modeling, content understanding,) to leverage their platforms and signals and work with them to collaborate on the adoption and evaluation of Responsible AI practices and ML Fairness tooling across Pinterest.\n Mentor junior engineers on the Responsible AI team and across the company on the R-AI space.\n Work with the team and senior leaders at the company to define and drive technical strategy in this area.\n \n What we're looking for: \n \n Extensive, real-world experience applying advanced ML methods to production systems, with a strong track record in responsible technology - spanning fairness, ethics, and broader societal considerations.\n Deep familiarity with cutting-edge ML architectures (e.g., transformer-based models, 2-tower architectures, LLMs) and their applications in large-scale Search and Recommender Systems.\n Proven ability to measure, deploy, and refine fairness interventions and broader Responsible AI solutions at scale, bridging state-of-the-art research with tangible product impact.\n 2+ years working experience in the engineering teams that build large-scale ML-driven user-facing products\n Masters or PhD in Comp Sci or related fields\n \n Nice to have: \n \n Publications at top ML conferences\n Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring\n Familiarity with LLM-powered productivity tools for","salary_min":138905,"salary_max":285982,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["generative-ai","code-generation","fine-tuning","llm","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8162046","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:29:11Z","expires_at":"2026-09-29T13:38:53.169272Z","created_at":"2026-08-29T13:39:28.752933Z","updated_at":"2026-08-30T13:38:53.311845Z","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/e82c02c8-1f82-44e8-a6a9-52c07aaa1af9"},{"id":"f14f7e06-beb3-4c13-ab57-c9011d97da6e","company_id":"47c8818e-9a45-4180-8d96-931d2774d36b","title":"Senior Software Engineer - Machine Learning Platform","slug":"senior-software-engineer-machine-learning-platform-34e43d2b","description":"About Upstart \n At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.\n As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.\n We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you’ll have the support to work in the way that works best for you.\n If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.\n The Team:  \n The Machine Learning and Simulations Platform (MLSP) team builds and operates the core infrastructure that powers ML model training,feature engineering,  inference, and marketplace simulation at Upstart. Every underwriting, fraud, conversion, and verification model runs on this platform. We own the full production path: the data and features that feed a model, the infrastructure that serves it at decision time, the tooling that deploys it, and the simulation systems that predict business impact before a change goes live.\n We are reimagining that platform to keep pace with our ML teams. That work spans low latency and GPU model serving, self-service model deployment, a feature platform that gives ML one place to define and serve production features, and high fidelity marketplace simulation. The team partners closely with ML, Engineering, Product, Data Platform.\n As a Senior Software Engineer on the ML and Simulations Platform team at Upstart, you will be responsible for building an MLOps platform to support machine learning model inference, process automation, model deployment, and observability. Machine Learning is critical to Upstart’s core business, and our greatest competitive advantage lies in the fact that we’re able to innovate on our AI engine quickly. You will also help build a  marketplace simulation platform to support rapid innovation across ML and Finance teams.\n How you’ll make an impact\n \n Build, maintain, and optimize  Upstart’s next-generation machine learning and simulation platform, enabling increased scale, performance, and confidence in decisioning.\n Develop  high-quality software applications that enable machine learning models to be applied to the ever-evolving needs of the business\n Build self-service tooling so ML teams can register features and deploy models independently, and reduce the manual work the platform team absorbs today.\n Deliver the data and feature infrastructure behind every model, including feature definition, storage, serving, and offline to online parity.\n Design and contribute to  our simulation systems to more accurately reflect production environments, reducing simulation cost and enabling broader usage across teams.\n Communicate closely with cross-functional partners from  ML, Engineering, Product, and Data Engineering  teams, keeping all stakeholders informed\n Mentor engineers across the team, sharing expertise on distributed systems,MLOps,  and scalable architecture.\n \n Minimum Qualifications  \n \n 6+ years of software engineering experience.\n Experience building and maintaining backend software services and APIs.\n Experience with distributed systems or large scale data processing, using Spark, Databricks, Ray, or an equivalent.\n Experience with an ML platform or the ML production path, such as training pipelines, model serving, feature pipelines, or a training data platform.\n Proficiency with some or many of the following: Python, Kotlin, Databricks, and AWS.\n Exhibits a growth mindset. You pick up new technologies that fit the task, and you learn from others.\n Ability to quickly comprehend complex requirements from ML, product, or engineering leadership, and translate them for both technical and non-technical partners.\n \n Preferred Qualifications \n \n Skill with Metaflow, MLflow, gRPC, Spark/PySpark, dbt, Ray, GPU\n Knowledge of simulation, experimentation, or ","salary_min":166900,"salary_max":230000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["api-design","mlops","distributed-systems","platform","machine-learning"],"apply_url":"https://careers.upstart.com/jobs?gh_jid=8161883","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:22:50Z","expires_at":"2026-09-29T13:45:21.136887Z","created_at":"2026-08-29T13:46:50.095597Z","updated_at":"2026-08-30T13:45:21.267566Z","company_name":"Upstart","company_slug":"upstart","company_logo_url":"https://www.google.com/s2/favicons?domain=upstart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f14f7e06-beb3-4c13-ab57-c9011d97da6e"},{"id":"1a525cd5-6fd1-4af5-be1b-eaa2a3e7709e","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior ML Engineer, Core Development","slug":"senior-ml-engineer-core-development-94a264b5","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the  expertise , technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed,  built  and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a  realtime , 3D  command  and control center. As the world enters an era of strategic competition, Anduril is committed to bringing  cutting-edge  autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.   \n About the Team:   Air Dominance \u0026 Strike designs, builds, and flies autonomous air vehicles—from  collaborative combat aircraft to expendable cruise missiles and counter-UAS  interceptors. Our vehicles move from whiteboard to first flight on timelines that  traditional primes consider impossible, which means our design cycles live or die  on how fast we can close the iteration loop. The Anduril AI Engineering team  exists to collapse that loop.    \n We are engineers first. We work from engineering first principles and unlock capability through machine learning and AI. We are building to scale across CFD, FEA, thermal, and electromagnetics, with pipelines, architectures, and validation practices that carry across programs.   \n   About the Job   We are looking for a Machine Learning Engineer to apply the latest research in  physics ML to the toughest bottlenecks in our design cycle. This role owns the  entire surrogate modeling stack for Air Dominance \u0026 Strike—the architectures,  the training infrastructure, the simulation data pipelines that feed it, and the  tooling design engineers use to consume predictions.    \n   You will develop, train, and deploy surrogate models that accelerate the physics simulations underpinning our air vehicle programs. Working alongside aerodynamicists, structures engineers, and thermal engineers, your models will directly inform decisions on hardware that actually flies. Where current methods fall short, you will develop new ones, with ample room to identify novel applications of physics ML across our portfolio.    \n   Defense experience is not required. We are looking for engineers who came to machine learning through the complex physical problems they were already trying to solve.    \n   This role is based onsite in our Costa Mesa, CA office.   \n   What You'll Do   \n \n Own the Surrogate Modeling Stack:  Drive the end-to-end design, training, and deployment of production-grade surrogate models to accelerate critical simulation workflows (CFD, FEA, thermal, structural, and aeroelastic) across air vehicle design.   \n Develop State-of-the-Art Architectures:  Design and implement neural architectures tailored to engineering physics, developing new techniques for uncertainty quantification, active learning, and inverse problems (such as geometry and shape optimization).   \n Build Robust Data \u0026 Training Infrastructure:  Create the pipelines behind the training—extracting, aggregating, and sanitizing tens of thousands of high-fidelity results from solver outputs.   \n Optimize \u0026 Integrate:  Optimize inference for the design loop (maximizing GPU utilization, batched evaluation, and interactive-speed latency) and seamlessly integrate surrogate predictions into the tooling our domain engineers already use.   \n Collaborate \u0026 Mentor:  Partner with domain engineers to identify where ML delivers the highest leverage, stay current with Physics AI research, and provide technical mentorship to non ML engineers.   \n \n Qualifications   \n \n Education:  BS, MS, or PhD in aerospace, thermal, mechanical, or electrical engineering, or in machine learning/AI/data science with a demonstrated engineering foundation.   \n Experience:  3+ years of experience taking ML models from R\u0026D into production using large-scale scientific or engineering datasets.   \n Physics ML Expertise:  Working knowledge of modern surrogate architectures (e.g. GNNs, Transolver, DoMINO \u0026 GeoTransolver) comb","salary_min":220000,"salary_max":292000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","tensorflow","distributed-systems","computer-vision","data-pipeline","pytorch","mlops","machine-learning"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5216691007?gh_jid=5216691007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T15:59:24Z","expires_at":"2026-09-29T13:37:22.907369Z","created_at":"2026-08-27T13:37:38.482223Z","updated_at":"2026-08-30T13:37:23.045327Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1a525cd5-6fd1-4af5-be1b-eaa2a3e7709e"},{"id":"a85f6a22-2c3d-4d63-bc92-fb6bab844254","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Senior Staff Machine Learning Systems Engineer, Ads ML Platform","slug":"senior-staff-machine-learning-systems-engineer-ads-ml-platform-555ec238","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 130 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 About Reddit Reddit is a community of communities, built on shared interests, passion, and trust. With 100,000+ active communities and 101M+ daily active unique visitors, we’re one of the largest sources of conversation and knowledge on the internet. For more information, visit redditinc.com .\n Team Overview \n The Ads ML Platform team builds infrastructure that accelerates high-scale ML systems and tooling for Ads ML, while extending reusable capabilities to broader Reddit ML use cases where appropriate. Our systems help ML engineers move faster across the full development lifecycle: creating features, generating training data, running offline experiments, validating model quality, launching production models, and operating ML systems reliably.\n We are looking for a Senior Staff Machine Learning Systems Engineer to lead the technical strategy for the end-to-end Ads ML engineer lifecycle. The initial focus will be on the feature development and training iteration loop: making it faster and easier for ML engineers to build features, generate reliable training data, run experiments, and move from idea to validated model improvement. Over time, this scope will expand into serving and online experimentation workflows, creating a more seamless path from offline iteration to production impact.\n This is a senior technical leadership role for someone who can combine deep systems expertise, production ML experience, architectural judgment, and cross-team influence.\n What You’ll Do \n \n Own the technical strategy for the end-to-end Ads ML engineer lifecycle, starting with feature development, training data, offline experimentation, and model iteration workflows.\n Align Ads ML platform priorities with Reddit’s broader ML Platform vision, translating Ads pain points into reusable platform capabilities where appropriate.\n Define architecture and technical standards for ML feature and training-data systems across batch/streaming computation, backfills, lineage, quality, observability, and online/offline consistency.\n Stay close to ML engineers and platform customers to identify high-leverage friction points and improve day-to-day development velocity.\n Build platform abstractions and workflow automation that make ML development faster, safer, more reliable, and more self-service.\n Over time, extend the platform strategy into serving and online experimentation workflows, creating a more seamless offline-to-online ML development experience.\n Partner across Ads, ML Platform, Data Platform, modeling, product, and engineering teams to clarify ownership, resolve ambiguity, and drive durable execution.\n Mentor Staff and senior engineers, raise the architecture and operational bar, and help grow the next generation of technical leaders.\n \n Who You Might Be \n \n You have 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems.\n You have 4+ years building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines.\n You have led broad, ambiguous, multi-team platform initiatives from strategy through adoption.\n You have built platforms used directly by ML engineers, data scientists, or product teams developing production ML systems.\n You have deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure.\n You have worked with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar technologies.\n You can balance urgent customer needs with durable long-term architecture and reusable platform patterns.\n You influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms.\n You are excited to shape how production ML systems are built, scaled, and operated, not only how models are trained.\n \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to ca","salary_min":292500,"salary_max":409500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["healthcare","distributed-systems","data-pipeline","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8157275","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T18:40:31Z","expires_at":"2026-09-29T13:38:59.415338Z","created_at":"2026-08-25T19:40:19.020618Z","updated_at":"2026-08-30T13:38:59.553319Z","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/a85f6a22-2c3d-4d63-bc92-fb6bab844254"},{"id":"385357c5-623e-4db0-baf5-06182c0f554f","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Staff Machine Learning Engineer, Emergency Trajectory Models","slug":"staff-machine-learning-engineer-emergency-trajectory-models-f0038f44","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The role  \n As a Staff Machine Learning Engineer in Wayve's AV Core organization, you will lead the technical direction and delivery of a learned emergency trajectory model for low-frequency, high-consequence maneuvers such as evasive steering and emergency braking. You will take the programme from problem definition through modelling, evaluation, integration, and evidence for deployment.\n Emergency maneuvers are rare, high-consequence events that place unusual demands on data, modelling, and validation. The hard problem is not simply to train another trajectory head: it is to define the operating envelope of a specialist model, what evidence shows that it improves outcomes without introducing new failure modes, and how it integrates with the general driving model and surrounding system. You will lead that work across AV Core and with partners across simulation, evaluation, safety, and product engineering.\n  \n Key responsibilities \n \n Set the technical strategy and roadmap for the emergency trajectory model, including its behavioral scope, operating envelope, system interfaces, and measurable acceptance criteria.\n Design and train trajectory-generating policies using the methods best supported by evidence, including behaviour cloning, reinforcement learning, or other sequential decision-making approaches.\n Build a data strategy for rare emergency cases, combining fleet data, targeted mining, simulation, augmentation, and reweighting while controlling coverage gaps and unintended behavior.\n Create rigorous open-loop and closed-loop evaluations for collision avoidance, evasive steering, emergency braking, recovery, robustness, latency, and regressions in nominal driving.\n Lead integration into the shared driving stack, align technical decisions across teams, and raise the bar through architecture reviews, mentoring, and clear communication of risks, trade-offs, and evidence.\n \n About you   \n In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n  \n Essential  \n \n A track record of staff-level technical leadership: setting direction for ambiguous machine learning programmes, aligning multiple teams, and carrying work from research through production deployment.\n Deep expertise developing learned trajectory-generation or policy models for embodied systems, including architecture design, objective design, training, and empirical validation.\n Hands-on experience with behaviour cloning, reinforcement learning, or related methods, including objective design, distribution shift, robustness, and closed-loop failure analysis.\n Strong machine learning engineering skills in Python and PyTorch, with experience building reproducible training and evaluation systems on large, heterogeneous datasets.\n Exceptional technical judgement and communication: able to make safety-relevant trade-offs explicit, define the evidence needed for decisions, and lead without relying on formal authority.\n \n  \n Desirable  \n \n Experience applying learned models in autonomous driving or robotics, with strong understanding of motion planning, vehicle dynamics, control, or collision avoidance.\n Experience with specialist, fallback, redundant, mixture-of-experts, or model-routing architectures and the interfaces used to select between them.\n Experience mining, generating, or evaluating rare events using simulation and fleet or real-world data.\n Experience deploying learned policies under real-time latency, reliability, and compute constraints; proficiency in C++, CUDA, or systems optimisation.\n Experience with multimodal, transformer-based, diffusion-based, or other generative trajectory or policy models.\n \n This is a full-time role based in our office in Sunnyvale.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and lear","salary_min":336400,"salary_max":370300,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","gpu","robotics","autonomous-vehicles","generative-ai","pytorch","machine-learning"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8747065002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T18:15:27Z","expires_at":"2026-09-29T13:43:29.618384Z","created_at":"2026-08-25T18:31:14.484268Z","updated_at":"2026-08-30T13:43:29.754954Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/385357c5-623e-4db0-baf5-06182c0f554f"},{"id":"fd34c611-28f9-40e5-9aeb-f4ba1b279373","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff ML Engineer, Foundation Models","slug":"software-engineer-foundation-models-e3613df0","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.\n This role follows a hybrid work schedule and reports to a Director of AI Foundations. \n You will: \n \n Work on connecting large-scale foundation models with production systems.\n Adapt our foundation models to new sensors, new platforms, and new production requirements.\n Collaborate extensively with other teams to land foundation models to our next-gen platforms.\n \n You have: \n \n Experience in building large-scale systems\n Track record of solving complex system problems\n Track record of successful deliveries through large-scale collaborations\n \n We prefer: \n \n Experience in LLM/VLM related foundation models or systems\n Experience with foundation model post-training\n \n  \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","reinforcement-learning","autonomous-vehicles","generative-ai","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8152556","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T19:15:00Z","expires_at":"2026-09-29T13:35:10.669553Z","created_at":"2026-08-25T18:27:25.923011Z","updated_at":"2026-08-30T13:35:10.807945Z","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/fd34c611-28f9-40e5-9aeb-f4ba1b279373"},{"id":"95641443-c7f1-443a-b4d7-ef763360667d","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Machine Learning Engineer","slug":"machine-learning-engineer-a0756d7f","description":"MACHINE LEARNING ENGINEER\n\n \n\nYou'll build the ML behind Firecrawl — the models and the systems that serve them. That starts with search: training and shipping the ranking and relevance models for one of our fastest-growing products, then extending that work across extraction quality and LLM-driven features. You'll also own how we measure: A/B testing launches and building the experimentation frameworks the whole team ships against. If you ship models into production — whether your title says ML engineer or data scientist — this is for you.\n\n \n\nSalary Range: $250,000–$290,000/year\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Onsite)\n\nJob Type: Full-Time \n\nExperience: 3+ years building ML or data-heavy systems in production \n\nVisa: Must be legally authorized to work in the United States. We're not able to sponsor visas right now, though that may change down the line.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data - the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nWe hit 8 figures in ARR in year one and more than doubled it in year two. We have 170k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.\n\n\n\n\nWHAT YOU'LL DO\n\n - Improve ranking and relevance for Firecrawl Search — from feature engineering to model training to production\n\n - Build and tune models for learning-to-rank, query understanding, and LLM-driven retrieval\n\n - Extend ML across Firecrawl's products — extraction quality, content classification, and evaluation of LLM-driven features\n\n - Mine query logs and behavioral data at scale to find where our products win and where they fail\n\n - Build the data pipelines that turn web-scale crawl and query data into training data and features\n\n - Work hands-on with platform, search engineers and cloud DevOps to get models running fast and cheap in production\n\n - Design and formulate our testing strategy — the A/B testing frameworks and offline evaluation the team ships against\n\n - Partner on product launches across Firecrawl: define success metrics, run the experiments, and make the ship/no-ship call on evidence\n\n - Report on how releases perform post-launch and turn the findings into the next iteration\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've shipped ML models into production systems and owned them after launch — deploying, monitoring, and retraining them, not handing them off\n\n - You have real ranking or relevance-modeling experience — learning-to-rank, recommendations, or search quality\n\n - You're comfortable in large, data-heavy systems: query logs, pipelines, and datasets that don't fit in memory\n\n - You write production-quality code (Python at minimum) and can work inside a real backend codebase\n\n - You're rigorous about measurement — you've designed and analyzed A/B tests and know when a lift is real\n\n - You can communicate results clearly to the team — what shipped, what moved, and what to do next\n\n\n\n\nNICE TO HAVE\n\n - MLOps experience — MLflow, experiment tracking, model registries, or feature stores; Kubernetes is a plus\n\n - Experience building or standardizing an experimentation framework at a previous company\n\n - Experience with embedding models, vector retrieval, or LLM-based relevance evaluation\n\n - Experience evaluating LLM outputs at scale — quality scoring, structured-extraction accuracy, or agent behavior\n\n - Spark or similar large-scale data processing experience\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - A pure statistician or analyst who needs an engineering team to productionize their work\n\n - Someone who wants to specialize narrowly and hand off everything else\n\n - Someone who optimizes for process over shipping\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense — $250,000–$290,000/year, based on impact, not tenure\n\n - Own a piece — Gain competitive equity in what you're helping build\n\n - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge\n\n - Parental l","salary_min":250000,"salary_max":290000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["search","data-pipeline","embeddings","mlops","agents","llm","machine-learning"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/72f9dc1d-65db-48c9-b3d9-c6ccdb997006/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T21:40:10.101Z","expires_at":"2026-09-29T13:45:43.966664Z","created_at":"2026-08-25T18:32:19.563403Z","updated_at":"2026-08-30T13:45:44.23876Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/95641443-c7f1-443a-b4d7-ef763360667d"},{"id":"ac071719-be76-48b4-a476-7a614bc11915","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Machine Learning Engineer II, Ads - Response Prediction","slug":"machine-learning-engineer-ii-ads-response-prediction-3e47bda7","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n About the Role: \n As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart's ads systems. You will use machine learning to devise and refine solutions in crucial areas such as ads selection, ranking, bidding, and auction across all of Instacart’s consumer facing surfaces and Ads Ecosystems. You will actively contribute to initiatives, assisting in all stages of ML projects from the initial concept, through prototyping and experimentation, to final launch.\n About the Team: \n The Ads Response Prediction team owns systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, Sequential Modeling and Generative Retrieval systems, LLM integrations, relevance models, pCTR models, bidding models and Foundation Models. The team optimizes for an efficient marketplace to ensure customers see ads that help them try new products/brands, advertisers boost their product sales for a good return on investment, and instacart generates the deserving revenue as well.\n  \n About the Job: \n \n Design, develop, and deploy machine learning solutions including data pipelines, model architectures and serving integrations to tackle practical challenges in the ads organization.\n Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., miss-calibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.\n Collaborate closely with product managers, data scientists, and infrastructure engineers to deeply understand business needs and create impactful ML applications.\n Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models.\n Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.\n \n  \n About You: \n Minimum Qualifications: \n \n Have a graduate degree (masters or PhD) in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.\n Have strong programming skills and fluency in data manipulation (SQL, Spark, Pandas) and Machine Learning (classical ML and Deep Learning) tools.\n Have strong analytical skills and problem-solving ability.\n Are a strong communicator who can collaborate with diverse stakeholders across all levels.\n \n  \n Preferred Qualifications: \n \n Have 1-2 years of industry experience using machine learning to solve real-world problems with large datasets.\n Knowledge of sequential modeling, Transformer architecture and Foundation Model.\n Familiarity with LLM integrations, agentic workflow and productivity tooling.\n Experience in building large scale online recommendation systems.\n \n #LI-Remote \n Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here . Currently, we are only hiring in the following provinces: Ontario, Alberta, British Columbia, and Nova Scotia.\n Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here .\n For Canadian based candidates, the base pay ranges for a successful candidate are listed below.\n CAN\n $154,000 — $162,500 CAD","salary_min":154000,"salary_max":162500,"location":"Remote (Canada)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"junior","tags":["agents","generative-ai","data-pipeline","fine-tuning","search","llm","deep-learning","machine-learning"],"apply_url":"https://instacart.careers/job/?gh_jid=8143263","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:14:19Z","expires_at":"2026-09-29T13:39:09.256769Z","created_at":"2026-08-25T18:28:59.889259Z","updated_at":"2026-08-30T13:39:09.402003Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ac071719-be76-48b4-a476-7a614bc11915"},{"id":"ea9ed310-83a0-4da7-92f0-3500ba5c05a5","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Machine Learning Engineer, Digital Twin Platform","slug":"senior-machine-learning-engineer-digital-twin-platform-081ecc8d","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n The Digital Twin Platform team at Instacart is on a mission to understand exactly what is on store shelves at all times — bringing the precision and depth of a smart warehouse to every local grocery store across North America. Inventory estimates power some of the most critical products at Instacart, from search to logistics, and this team sits at the center of it all. Operating like a startup within a larger company, the team drives the end-to-end shelf data supply chain: ingesting data from retail partners, actively collecting novel inventory observations, developing sophisticated models, and integrating those outputs into live products at scale.\n We are looking for a Senior Machine Learning Engineer to help build the next generation of platforms for understanding, observing, and predicting inventory levels and in-store stocking dynamics in real time. In this role, you will develop machine learning models and deploy them into production systems, working in close collaboration with software engineers, computer vision engineers, product leads, and data scientists. If you're motivated by technically complex, high-impact problems and want to see your work shape how millions of people experience grocery shopping, this is the role for you.\n You can read more about some of the work this team is doing here:\n Introducing New Enterprise AI Solutions to Democratize AI for Grocers of All Sizes \n Instacart Acquires Arpalus to Advance Real-Time Shelf Intelligence \n About the Job \n \n Design, develop, and deploy machine learning models that power real-time understanding of in-store inventory levels and shelf stocking dynamics across thousands of retail locations at scale.\n Own the full ML lifecycle — from problem framing and data exploration through model training, evaluation, and production deployment — with a focus on quality, reliability, and measurable business impact.\n Collaborate cross-functionally with software engineers, computer vision engineers, data scientists, and product leads to bring cutting-edge technologies to the team and drive new product innovation.\n Contribute to building and evolving the core infrastructure of the Digital Twin Platform, including systems that ingest data from retail partners and actively collect novel inventory observations to feed the modeling pipeline.\n Help define the technical direction of an expanding modeling practice on a high-performance team that operates with startup speed — expect ambiguity, changing priorities, and the opportunity to make a significant mark on a problem space that is core to Instacart's business.\n \n About You \n Minimum Qualifications\n \n 5+ years of experience developing and deploying machine learning models in production environments.\n Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.\n Demonstrated experience with large-scale data pipelines and working with structured and unstructured data at scale.\n Experience with cloud infrastructure (AWS, GCP, or Azure) and familiarity with ML platform tooling for model training, versioning, and serving.\n Bachelor's degree or higher in Computer Science, Machine Learning, Statistics, or a related technical field, or equivalent practical experience.\n \n Preferred Qualifications\n \n Experience working on computer vision, inventory forecasting, demand sensing, or related spatial/temporal modeling problems.\n Familiarity with real-time inference systems and the architectural considerations of serving ML models in low-latency, high-throughput environments.\n Prior experience in a fast-paced, cross-functional environment where you have independently driven projects from conception through production with limited oversight.\n Exposure to retail, supply chain, or e-commerce domains and an understanding of how inventory data flows an","salary_min":206000,"salary_max":217500,"location":"Remote (Canada)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["pytorch","tensorflow","data-pipeline","computer-vision","fine-tuning","cloud","machine-learning"],"apply_url":"https://instacart.careers/job/?gh_jid=8143147","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:12:31Z","expires_at":"2026-09-29T13:39:09.843919Z","created_at":"2026-08-25T18:28:59.914166Z","updated_at":"2026-08-30T13:39:09.978396Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ea9ed310-83a0-4da7-92f0-3500ba5c05a5"},{"id":"f4154f18-d97b-44e2-af31-0a213ace915e","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Machine Learning Engineer, Digital Twin Platform","slug":"senior-machine-learning-engineer-digital-twin-platform-85efdb23","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n The Digital Twin Platform team at Instacart is on a mission to understand exactly what is on store shelves at all times — bringing the precision and depth of a smart warehouse to every local grocery store across North America. Inventory estimates power some of the most critical products at Instacart, from search to logistics, and this team sits at the center of it all. Operating like a startup within a larger company, the team drives the end-to-end shelf data supply chain: ingesting data from retail partners, actively collecting novel inventory observations, developing sophisticated models, and integrating those outputs into live products at scale.\n We are looking for a Senior Machine Learning Engineer to help build the next generation of platforms for understanding, observing, and predicting inventory levels and in-store stocking dynamics in real time. In this role, you will develop machine learning models and deploy them into production systems, working in close collaboration with software engineers, computer vision engineers, product leads, and data scientists. If you're motivated by technically complex, high-impact problems and want to see your work shape how millions of people experience grocery shopping, this is the role for you.\n You can read more about some of the work this team is doing here:\n Introducing New Enterprise AI Solutions to Democratize AI for Grocers of All Sizes \n Instacart Acquires Arpalus to Advance Real-Time Shelf Intelligence \n About the Job \n \n Design, develop, and deploy machine learning models that power real-time understanding of in-store inventory levels and shelf stocking dynamics across thousands of retail locations at scale.\n Own the full ML lifecycle — from problem framing and data exploration through model training, evaluation, and production deployment — with a focus on quality, reliability, and measurable business impact.\n Collaborate cross-functionally with software engineers, computer vision engineers, data scientists, and product leads to bring cutting-edge technologies to the team and drive new product innovation.\n Contribute to building and evolving the core infrastructure of the Digital Twin Platform, including systems that ingest data from retail partners and actively collect novel inventory observations to feed the modeling pipeline.\n Help define the technical direction of an expanding modeling practice on a high-performance team that operates with startup speed — expect ambiguity, changing priorities, and the opportunity to make a significant mark on a problem space that is core to Instacart's business.\n \n About You \n Minimum Qualifications\n \n 5+ years of experience developing and deploying machine learning models in production environments.\n Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.\n Demonstrated experience with large-scale data pipelines and working with structured and unstructured data at scale.\n Experience with cloud infrastructure (AWS, GCP, or Azure) and familiarity with ML platform tooling for model training, versioning, and serving.\n Bachelor's degree or higher in Computer Science, Machine Learning, Statistics, or a related technical field, or equivalent practical experience.\n \n Preferred Qualifications\n \n Experience working on computer vision, inventory forecasting, demand sensing, or related spatial/temporal modeling problems.\n Familiarity with real-time inference systems and the architectural considerations of serving ML models in low-latency, high-throughput environments.\n Prior experience in a fast-paced, cross-functional environment where you have independently driven projects from conception through production with limited oversight.\n Exposure to retail, supply chain, or e-commerce domains and an understanding of how inventory data flows an","salary_min":201000,"salary_max":212000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["cloud","pytorch","computer-vision","fine-tuning","tensorflow","data-pipeline","machine-learning"],"apply_url":"https://instacart.careers/job/?gh_jid=8143145","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:12:30Z","expires_at":"2026-09-29T13:39:09.752316Z","created_at":"2026-08-25T18:28:59.910126Z","updated_at":"2026-08-30T13:39:09.886675Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f4154f18-d97b-44e2-af31-0a213ace915e"},{"id":"6aabed50-4dbf-497c-bf63-c12df13dba87","company_id":"f36ec848-cb19-4b95-a680-6733e58086c0","title":"Lead Machine Learning Engineer","slug":"lead-machine-learning-engineer-d35ab649","description":"May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think. Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We’re building the world’s best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we’ve given more than 500,000 autonomous rides to real people around the globe. And we’re just getting started. We’re hiring people who share our passion for building the future, today, solving real-world problems and seeing the impact of their work. Join us. \n We are seeking Machine Learning Leaders in the Autonomous Vehicle domain. As part of our team, you will play a critical role in enhancing May’s Machine Learning capabilities both on and off the vehicle, in a commercial large-scale environment with high standards of quality.\n Essential Responsibilities \n \n Design, train and evaluate state of the art models for May’s autonomous driving, simulation and ML Platform stack.\n Leverage emerging techniques in the End-to-End driving, Vision Language Action (VLA), World or Foundation model domains to solve commercial-scale problems.  \n Lead small teams of cross functional Engineers beyond the state of the art.\n Define data balance, training experiment and evaluation practices to train efficiently at petabyte scale.\n \n Skills and Abilities \n Success in this role typically requires the following competencies:\n \n Direct experience architecting \u0026 training VLA, MMLM, or Generative World Models for commercial-scale applications\n Experience composing, processing and characterizing large (\u003e100TB) multi-modal datasets\n Experience analyzing and addressing long-tail failure cases in large models\n Experience leading teams of 2-3 Engineers and communicating technical details to interdisciplinary leadership.\n \n Qualifications and Experience \n Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:\n Required \n \n Extensive practical experience in one of the following domains:\n \n Vision Language Action Models\n Generative World Models\n Foundation Models in Robotics\n Data Centric AI\n \n A minimum of 4 years of industry experience working on commercial robotics systems.\n A minimum of 1 year mentoring ML Engineers in a commercial or lab environment.\n Master’s degree in Robotics, Computer Science, or Computer Engineering, or a field that requires a strong mathematical and/or engineering foundation.\n Practical experience handling the “Long Tail” problem in Machine Learning.\n Strong programming skills in Python/PyTorch in a Linux environment.\n Functional understanding of LiDAR, Camera and Radar processing techniques.\n \n Desirable \n \n PhD and/or published research in the described specialty domains.\n Familiar with common post-training techniques.\n Experience deploying models to resource constrained and edge hardware\n Functional understanding of C/C++/CUDA memory and threading models.\n \n Physical Requirements \n \n Standard office working conditions which includes but is not limited to:\n \n Prolonged sitting\n Prolonged standing\n Prolonged computer use\n \n Travel required? -  Low: 5%-10%\n \n \n \n \n \n \n \n \n Benefits and Perks \n \n Comprehensive healthcare suite including medical, dental, vision, life, and disability plans. Domestic partners who have been residing together at least one year are also eligible to participate. \n Health Savings and Flexible Spending Healthcare and Dependent Care Accounts available.\n Rich retirement benefits, including an immediately vested employer safe harbor match.\n Generous paid parental leave as well as a phased return to work. \n Flexible vacation policy in addition to paid company holidays.\n Total Wellness Program providing numerous resources for overall wellbeing   \n \n Don’t meet every single requirement? Studies have shown that women and/or people of color are less likely to apply to a job unless they meet every qualification. At May Mobility, we’re committed to building a diverse, inclusive, and authentic workforce, so if you’re excited about this role but your previous experience doesn’t align perfectly with every qualification, we encourage you to apply anyway! You may be the perfect candidate for this or another role at May.\n Want to learn more about our culture \u0026 benefits? Check out our  website ! \n May Mobility is an equal opportunity employer.  All applicants for employment will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual ","salary_min":220000,"salary_max":270000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["healthcare","autonomous-vehicles","generative-ai","robotics","pytorch","gpu","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/maymobility/jobs/8730957002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-19T19:14:18Z","expires_at":"2026-09-29T13:47:53.465471Z","created_at":"2026-08-25T18:33:06.96684Z","updated_at":"2026-08-30T13:47:53.592694Z","company_name":"May Mobility","company_slug":"may-mobility","company_logo_url":"https://www.google.com/s2/favicons?domain=maymobility.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6aabed50-4dbf-497c-bf63-c12df13dba87"},{"id":"b040d81d-82bc-4198-a020-8cf6e2705157","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Scientist, Applied Causal Inference","slug":"staff-machine-learning-scientist-applied-causal-inference-4231759e","description":"About the Team \n DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. These businesses operate in high-dimensional, messy marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question.\n About the Role \n We are hiring a Causal Machine Learning Engineer to help build the causal ML foundation behind how DoorDash grows New Verticals. This is not a generic ML role with some experimentation work on the side. We are looking for someone who has built or deeply worked on production causal systems: uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization, or marketplace decisioning systems.\n You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering. The mandate is to build the causal spine for a large-scale consumer marketplace.\n You're excited about this opportunity because you will… \n \n Design, build, and productionize causal ML systems that influence real marketplace decisions across New Verticals.\n Build uplift / heterogeneous treatment effect models for consumer lifecycle value, promotions, retention, and reactivation.\n Develop counterfactual evaluation frameworks for ranking, recommendations, search, promotions, substitutions, and marketplace interventions.\n Build systems that connect experimentation, observational data, and ML decisioning so teams can make better tradeoffs when randomized experiments are slow, noisy, or incomplete.\n Design surrogate metrics and early indicators that help teams move faster while preserving long-term marketplace health.\n Partner with econometrics and analytics leaders to choose the right methods: doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED-style variance reduction, contextual bandits, off-policy evaluation, and related approaches.\n Translate causal models into production systems that can shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.\n Raise the bar for causal reasoning across ML teams: when to trust a model, when not to, and how to debug causal claims in a real marketplace.\n \n We're excited about you because you have… \n \n Deep practical experience with causal inference, econometrics, experimentation, or causal ML .\n Experience shipping models or decision systems in production, ideally in consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, fintech, or other high-scale settings.\n Strong judgment around the tradeoffs between randomized experiments, observational estimation, and model-based decisioning.\n Comfort debating and applying methods such as doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation .\n Strong ML engineering ability: you can build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production.\n Strong product judgment: you can connect methods to business decisions, not just optimize offline metrics.\n The ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","healthcare","cloud","payments","machine-learning","inference"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8140067","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T20:55:11Z","expires_at":"2026-09-29T13:49:21.704976Z","created_at":"2026-08-25T18:33:45.109934Z","updated_at":"2026-08-30T13:49:21.836357Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b040d81d-82bc-4198-a020-8cf6e2705157"},{"id":"e9ebce41-3ac8-47ba-a5ee-5e22e28ef4f1","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Causal Inference","slug":"staff-machine-learning-engineer-causal-inference-da3f0671","description":"About the Team \n DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. These businesses operate in high-dimensional, messy marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question.\n About the Role \n We are hiring a Causal Machine Learning Engineer to help build the causal ML foundation behind how DoorDash grows New Verticals. We are looking for someone who has built or deeply worked on production causal systems: uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization, or marketplace decisioning systems.\n You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering. The mandate is to build the causal spine for a large-scale consumer marketplace.\n You're excited about this opportunity because you will… \n \n Design, build, and productionize causal ML systems that influence real marketplace decisions across New Verticals.\n Build uplift / heterogeneous treatment effect models for consumer lifecycle value, promotions, retention, and reactivation.\n Develop counterfactual evaluation frameworks for ranking, recommendations, search, promotions, substitutions, and marketplace interventions.\n Build systems that connect experimentation, observational data, and ML decisioning so teams can make better tradeoffs when randomized experiments are slow, noisy, or incomplete.\n Design surrogate metrics and early indicators that help teams move faster while preserving long-term marketplace health.\n Partner with econometrics and analytics leaders to choose the right methods: doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED-style variance reduction, contextual bandits, off-policy evaluation, and related approaches.\n Translate causal models into production systems that can shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.\n Raise the bar for causal reasoning across ML teams: when to trust a model, when not to, and how to debug causal claims in a real marketplace.\n \n We're excited about you because you have… \n \n Deep practical experience with causal inference, econometrics, experimentation, or causal ML .\n Experience shipping models or decision systems in production, ideally in consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, fintech, or other high-scale settings.\n Strong judgment around the tradeoffs between randomized experiments, observational estimation, and model-based decisioning.\n Comfort debating and applying methods such as doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation .\n Strong ML engineering ability: you can build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production.\n Strong product judgment: you can connect methods to business decisions, not just optimize offline metrics.\n The ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/mon","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["cloud","healthcare","payments","fine-tuning","machine-learning","inference"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8139942","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T20:55:10Z","expires_at":"2026-09-29T13:49:21.417077Z","created_at":"2026-08-25T18:33:45.096852Z","updated_at":"2026-08-30T13:49:21.548155Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e9ebce41-3ac8-47ba-a5ee-5e22e28ef4f1"},{"id":"e528a562-2c86-4037-b54d-8daa64393e83","company_id":"47c8818e-9a45-4180-8d96-931d2774d36b","title":"Staff Machine Learning Model Risk Specialist","slug":"staff-machine-learning-model-risk-specialist-dbe9dbcd","description":"About Upstart \n At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.\n As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.\n We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you’ll have the support to work in the way that works best for you.\n If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.\n The Team:  \n Upstart’s Model Risk team is responsible for ensuring that the risk of models — including all models impacting the new Upstart Bank — is well-understood, monitored, and mitigated. For years, machine learning (ML) models have been the key, differentiating technology at Upstart and an exciting area of focus for the team, but we are also expanding our scope to include all modeling methodologies and Generative AI applications across the Bank. This work is essential for Upstart’s internal risk management, ensuring that our models help us make better decisions and maintain credibility with external stakeholders such as our regulators and capital providers. The team’s focus is on articulating sound model risk management principles and implementing them in collaboration with our peers on Upstart’s Risk and Machine Learning teams. This work also includes explaining our models to stakeholders, supporting external validations, and conducting analyses to reinforce our goals.\n As a Staff Model Risk Specialist at Upstart, you will independently execute core components of the model risk management program supporting Upstart Bank. You will oversee risk across a diverse and growing inventory of models and Generative AI applications, including sophisticated machine learning models used in lending and other models supporting areas such as fraud, compliance, finance, capital and liquidity, servicing, and operational risk.\n This presents a unique opportunity to help build a comprehensive model risk management program for a new bank. You will evaluate model and GenAI application documentation, monitoring, governance, and risk assessments while partnering with developers, business sponsors, and risk stakeholders to identify and address emerging risks. You will apply a risk-based approach across technologies that range from traditional statistical methods to advanced machine learning and GenAI systems, adapting your review to their different purposes, complexities, and risk profiles. You will also translate complex technical concepts into clear, decision-useful information for audiences with varying levels of technical expertise.\n  \n How you’ll make an impact \n \n Partner with Machine Learning teams, GenAI application developers, business sponsors, and other stakeholders to maintain accurate inventories, risk assessments, documentation, monitoring reports, and supporting governance materials for models and GenAI applications affecting Upstart Bank.\n Review methodologies, assumptions, data inputs, system designs, performance measures, controls, and limitations to provide effective challenge and identify areas requiring further analysis or remediation.\n Apply a risk-based approach to evaluate a broad range of quantitative methods and technologies, from traditional statistical and financial models to complex machine learning models and GenAI applications.\n Conduct and document model risk assessments, monitoring reviews, and targeted quantitative analyses that support internal policies and regulatory expectations.\n Help develop practical governance approaches for new and rapidly evolving technologies, particularly machine learning and GenAI applications for which risks, evaluation methods, and industry practices continue to evolve.\n Respond to model- and GenAI-related questions from regulators, lending partners, and other external stakeh","salary_min":140300,"salary_max":175000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["generative-ai","deep-learning","rag","mlops","agents","machine-learning"],"apply_url":"https://careers.upstart.com/jobs?gh_jid=8140033","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T19:21:29Z","expires_at":"2026-09-29T13:45:21.327262Z","created_at":"2026-08-25T18:31:45.395594Z","updated_at":"2026-08-30T13:45:21.461964Z","company_name":"Upstart","company_slug":"upstart","company_logo_url":"https://www.google.com/s2/favicons?domain=upstart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e528a562-2c86-4037-b54d-8daa64393e83"},{"id":"b04c0a03-fc25-4cc1-85e1-9c6efc357643","company_id":"97187e1c-a220-4e7e-aa1e-cd5342f434c1","title":"Machine Learning Engineer","slug":"machine-learning-engineer-50e35dc4","description":"Who we are \n About Stripe \n Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.\n What you’ll do \n Responsibilities \n \n Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints. \n Design systems to speed up the time from idea to deployment of new models. \n Experiment and iterate on ML models (using tools including PyTorch and TensorFlow) to achieve key business goals and drive efficiency. \n Develop pipelines and automated processes to train and evaluate models in offline and online environments. \n Integrate ML models into production systems and ensure their scalability and reliability. \n Collaborate with product and strategy partners to propose, prioritize, and implement new product features. \n Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions.\n \n Who you are \n Minimum requirements \n Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of experience in Building and shipping ML systems in production.\n Must have two (2) years of experience in each of the following:\n \n ML algorithms and model architectures;\n Designing, training and evaluating machine learning models;\n Productionizing and deploying machine learning models at scale;\n Orchestrating data pipelines and leveraging large-scale datasets; and\n Building and deploying ML models to solve business problems.\n \n Must have one (1) year of experience in each of the following:\n \n ML libraries and frameworks including PyTorch, TensorFlow, XGBoost or Spark; and\n Deep learning, including transformers, test-time compute, or reinforcement learning.\n \n Salary: $212,000 - $318,000/yr.  \n This salary range represents the base salary range for the role and any sales commissions / sales bonuses targets, if applicable, would be in addition to the base salary.\n 40 hrs/week\n 50% Telecommuting Permitted.\n Multiple Positions Available. \n Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. CA29 \n #LI-DNI","salary_min":212000,"salary_max":318000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","tensorflow","pytorch","deep-learning","data-pipeline","payments","machine-learning"],"apply_url":"https://stripe.com/jobs/search?gh_jid=8137997","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-17T22:06:29Z","expires_at":"2026-09-29T13:34:35.944205Z","created_at":"2026-08-25T18:27:17.935027Z","updated_at":"2026-08-30T13:34:36.087082Z","company_name":"Stripe","company_slug":"stripe","company_logo_url":"https://www.google.com/s2/favicons?domain=stripe.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b04c0a03-fc25-4cc1-85e1-9c6efc357643"},{"id":"d8eb6e88-6cb4-4d52-96e3-30a33b5d446e","company_id":"c93e0284-9c76-4a85-9905-494865ab9278","title":"Senior Principal Machine Learning Engineer","slug":"senior-principal-machine-learning-engineer-1289db26","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 team\n The ML team builds and optimizes the models that run on SambaNova's RDU accelerators. Their work covers model architecture, training and fine-tuning, inference optimization, evaluation, and data curation, and it lands in SambaStack and SambaCloud. They work directly with the compiler, systems, and hardware teams on co-design, so decisions about a model shape decisions about the silicon it runs on.\n About the role \n As a Senior Principal Machine Learning Engineer, you will be responsible for designing, developing, and optimizing machine learning models—with a focus on cutting-edge Large Language Models (LLMs)—to run efficiently on SambaNova's specialized hardware architecture, including the RDU. This critical role bridges advanced LLM research and practical deployment, involving the development of model architectures, improving training and inference efficiency, and collaborating on hardware-software co-design with compiler, systems, and hardware teams. The engineer will also act as the ML expert, guiding the integration of LLM solutions into production systems and customer-facing products like SambaStack and SambaCloud, with work spanning the full lifecycle from training and inference to evaluation and data curation.\n Responsibilities \n Some of your responsibilities will include:\n \n Define and drive technical strategy for ML model development, training pipelines, and inference systems on SambaNova's RDU and broader hardware ecosystem\n Lead hardware-software co-design efforts in close collaboration with compiler, systems, and hardware teams—shaping architectural decisions that unlock performance at scale\n Identify, evaluate, and champion state-of-the-art ML techniques (e.g., speculative decoding, reinforcement learning, mixture-of-experts, long-context modeling) for adoption and adaptation on reconfigurable dataflow architectures\n Serve as the senior technical voice in critical design reviews, architectural decisions, and cross-functional planning—providing guidance that influences product and engineering roadmaps\n Mentor and develop principal and senior ML engineers, elevating the technical capabilities of the organization through active collaboration, design feedback, and knowledge transfer\n Partner with product and engineering leadership to translate complex ML capabilities into scalable, customer-facing solutions in SambaStack and SambaCloud\n Drive resolution of the most complex, ambiguous technical challenges —including those that span organizational boundaries or require novel approaches not yet established in the field\n \n Required Qualifications \n \n B.S. in Computer Science, Electrical Engineering, or related field\n 8+ years of industry experience in machine learning engineering, with a demonstrated record of technical leadership on large-scale or novel ML systems\n Deep expertise in LLM training, fine-tuning, inference optimization, and evaluation at scale\n Strong background in ML algorithms, deep learning architectures, and modern training methodologies, with the ability to critically evaluate and advance the state of the art\n Demonstrated ability to lead and align cross-functional technical efforts, mentor senior engineers, and influence organizational direction without direct management authority\n Track record of independently scoping and delivering high-complexity, high-ambiguity technical projects\n \n Preferred Qualifications \n \n M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field\n Experience with hardware-software co-design with non-GPU accelerators\n Publications or open-source contributions in LLM training or inference\n Experience with speculative decoding, mixture-of-experts, or long-context modeling in production\n Experience with reinforcement learning for post-training\n Familiarity with compiler or kernel-level optimization for ML workloads\n Base Salary Range:\n Base Pay Range\n $220,000 — $300,000 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 SambaN","salary_min":220000,"salary_max":300000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["deep-learning","llm","fine-tuning","generative-ai","reinforcement-learning","machine-learning"],"apply_url":"https://sambanova.ai/sambanova-available-positions/?gh_jid=6089843004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-16T15:41:38Z","expires_at":"2026-09-29T13:34:49.513266Z","created_at":"2026-08-25T18:27:21.867478Z","updated_at":"2026-08-30T13:34:49.649206Z","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/d8eb6e88-6cb4-4d52-96e3-30a33b5d446e"},{"id":"f5ed0945-789e-4523-9377-609285671969","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Machine Learning Engineer, Trust","slug":"senior-machine-learning-engineer-trust-af806995","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 Everyone at Airbnb thinks about trust, but our team obsesses over it daily. At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe. The Trust team is responsible for developing the technology that helps protect our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards. We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.). We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community.\n The Trust Frontier AI team is where new AI technology for Trust gets invented and proven. We build specialized models for mission-critical trust and safety problems, develop the AI agents and agentic capabilities that automate trust decisions, and create the benchmarks and evaluation harnesses that keep decision quality high as those agents take on more autonomy. We work on problems before the answer is known — prototyping, experimenting, and iterating with our partner teams until a solution proves itself against real business and top line metrics.\n You'll work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams. Together, you'll design and build ML solutions that have direct, meaningful impact on user trust, business success, and the global Airbnb community.\n The Difference You Will Make: \n As a Senior Machine Learning Engineer on the Trust Frontier AI team, you will actively contribute code and ideas that shape the next generation of AI systems protecting millions of Airbnb users. You'll own and deliver ML projects end-to-end, from framing an ambiguous problem and prototyping a solution, to training and productionizing models, to proving impact on top line metrics with front line teams.\n You'll work on abuse behavior detection that spans multiple defenses, on AI agents that make trust decisions autonomously, and on the evaluation and benchmarking work that makes those decisions trustworthy. Much of this work is early: you will help decide what to build, not only how to build it, and you'll see it through to measurable impact on the platform.\n A Typical Day:  \n \n Frame and prototype ML and agentic solutions for problems that do not yet have an established approach, in partnership with product managers, data scientists, and front line defense teams.\n Design, build, and productionize end-to-end Machine Learning pipelines — including feature engineering, model training, evaluation, and deployment — for both batch and real-time use cases.\n Build and improve abuse behavior detection that generalizes across defenses.\n Design, launch, and iterate on AI agents that automate trust decisions, including orchestration, tool interfaces, and the guardrails that hold quality steady as autonomy increases.\n Build benchmarks, evaluation harnesses, and instrumentation that let us measure agentic and model decision quality objectively, and use them to drive real improvements.\n Develop specialized models for trust and safety use cases, and use LLMs and AI agents to accelerate how we build models.\n Write, review, and ship clean, testable code — whether training a new model, improving an existing pipeline, or optimizing a feature for scalability and reliability.\n Work with large-scale structured and unstructured data to continuously improve ML models for Airbnb product, business, and operational use cases.\n Partner with front line defense teams to validate solutions through experiments and holdouts, and quantify their impact on business and operational metrics.\n Participate in code reviews, design discussions, and cross-team collaborations to contribute to a high-quality ML engineering culture.\n \n Your Expertise: \n \n 5-10 years of industry experience in applied Machine Learning, with a track record of building and productionizing models at scale.\n 1-2+ years of hands-on experience with LLMs and GenAI technologies, including building with agentic frameworks, orchestration, and evaluation.\n Strong programming skills in Python (required) and familiarity with Scala, Java, or equivalent.\n Solid understanding of Machine Learning best practices — e.g., training/serving skew minimization, A/B testing, feature engineering, model selection — and algorithms such as ","salary_min":200000,"salary_max":235000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","data-pipeline","agents","pytorch","generative-ai","llm","deep-learning","machine-learning"],"apply_url":"https://careers.airbnb.com/positions/8130355?gh_jid=8130355","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-14T21:27:57Z","expires_at":"2026-09-29T13:39:39.773826Z","created_at":"2026-08-25T18:29:15.515072Z","updated_at":"2026-08-30T13:39:39.906905Z","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/f5ed0945-789e-4523-9377-609285671969"},{"id":"05a2a4c1-155f-4f2a-b2e9-4acdac1c396f","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Staff Machine Learning Engineer, Traffic Intelligence","slug":"staff-machine-learning-engineer-traffic-intelligence-a539b99f","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 Our web and API surfaces handle requests from guests and hosts alongside a growing volume of automated agents: AI assistants, crawlers, and scrapers. We build the systems that bring clarity to this traffic, combining in-house ML and vendor signals to decide in real time how to serve billions of daily requests. Anti-bot and anti-scraping detection is our most adversarial mandate, but the wider challenge is full traffic classification: building evaluation frameworks that tell legitimate automation apart from abusive actors, so high-stakes decisions hold up across the fleet.\n The Difference You Will Make: \n You will architect and maintain Airbnb’s end-to-end traffic classification ML systems, balancing high-performance model deployment with rigorous offline data pipelines. Success is measured by your ability to harden edge-traffic policies—targeting reduced bot-incident MTTM—and by establishing rigorous evaluation practices that ensure foundational signal accuracy and evasion-resistance across the fleet. \n A Typical Day:  \n \n Own the complete lifecycle of traffic-scoring models, from problem framing to real-time deployment, managing the adversarial feedback loop to ensure high evasion-resistance and directly drive reductions in bot-incident MTTM.\n Architect robust offline-to-online pipelines that produce certified source-of-truth datasets, establishing rigorous evaluation frameworks—such as stratified benchmarks and leakage-prevention checks—to ensure every model improvement is empirically measurable and defensible.\n Execute model optimization within strict millisecond latency budgets at the internet edge, uniquely balancing inference costs against incremental value while maintaining fleet-wide fail-open behaviors.\n Partner daily with security analysts, data platform engineers, and international infrastructure partners to integrate scoring intelligence into automated mitigation workflows, ensuring global consistency in traffic classification despite regional failovers or CDN updates.\n Serve as the team’s machine learning authority, communicating complex model trade-offs to leadership and cross-functional teams to translate technical research into practical, scalable engineering guidance.\n \n Your Expertise: \n \n 9+ years of applied experience in production ML, specifically within non-stationary, adversarial domains (e.g., traffic integrity, bot mitigation, or fraud) where you have managed the feedback loop against adaptive actors.\n Demonstrated experience architecting scalable, offline-to-online data pipelines that produce certified source-of-truth datasets for low-latency inference systems.\n Strong foundation in rigorous model evaluation, including metrics like ROC/AUC, precision/recall, and calibration, with an ability to communicate complex trade-offs to cross-functional stakeholders.\n Experience with large-scale data engineering (warehouse-scale SQL) and feature engineering on high-volume event streams to build reliable, production-ready modeling pipelines.\n Practical knowledge of internet edge infrastructure (e.g., CDN/load balancer behavior, HTTP/TLS signatures) and their role in verifying foundational signals.\n Proven track record of cross-functional leadership, landing initiatives through shared datasets and consumer contracts while mentoring junior engineers on technical quality and design practices.\n MS/PhD in a quantitative field (e.g., Statistics, ML) or equivalent deep engineering experience, with significant ownership of large-scale systems measuring evasion-resistance.\n Preferred: \n PhD in Statistics, Mathematics, Machine Learning, or a related quantitative discipline.\n Advanced expertise in graph-based coordination or Sybil network detection methods for complex, distributed system analysis.\n Deep experience with causal or econometric methods to model the business impact of false positives on legitimate user traffic.\n Experience implementing Bayesian calibration techniques for handling adversarially-biased, sparse, or imbalanced datasets.\n Familiarity with data governance practices and platform engineering, specifically managing the lifecycle of certified datasets and downstream consumer contracts.\n Exposure to LLM agent tooling and benchmarking, with a focus on optimizing inference costs against latency and value trade-offs.\n \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 register","salary_min":212000,"salary_max":265000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","llm","mlops","distributed-systems","machine-learning"],"apply_url":"https://careers.airbnb.com/positions/8129371?gh_jid=8129371","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-13T23:22:22Z","expires_at":"2026-09-29T13:39:40.744787Z","created_at":"2026-08-25T18:29:15.56457Z","updated_at":"2026-08-30T13:39:40.88201Z","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/05a2a4c1-155f-4f2a-b2e9-4acdac1c396f"},{"id":"c78452e5-7fd6-46e4-931b-4317dab14624","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Perception Machine Learning Engineer - Continuous Learning","slug":"perception-machine-learning-engineer-continuous-learning-f4011dc8","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 As a Perception Machine Learning Engineer, you will build the intelligent systems that \"see\" the world, directly shaping the future of autonomous travel.\n Within the Perception team, we are tackling some of the most complex, open-ended challenges in autonomous driving. Our models must constantly adapt and improve as our fleet encounters the vast, unpredictable realities of public roads. We are looking for a Machine Learning Engineer to help design and build the automated, closed-loop systems that drive this continuous improvement.\n In this role, you will be the bridge between model architecture and large-scale data infrastructure. You will leverage active learning and sophisticated data curation strategies to ensure our perception models are always learning from the most informative examples. Crucially, this means managing the entire lifecycle of our data: intelligently selecting novel scenarios from the fleet while continuously pruning our existing corpus to maximize training efficiency.\n In this hybrid role you will report to a Technical Lead Manager.\n You will: \n \n Architect Infrastructure: Design and scale the data pipelines needed to mine, ingest, and manage massive volumes of sensor data from our fleet.\n Drive Model Improvement: Deploy active learning algorithms to continuously identify and select the most impactful data for training, ensuring our large models continuously adapt to new environments with incremental updates.\n Ensure Model Quality: Develop methods and recipes for evaluating real-world performance of our models, and detecting regressions in model updates.  Develop and maintain ground-truth free performance metrics.\n Optimize Data Efficiency: Conduct large-scale experiments focused on data balancing, subset selection, and label quality optimization. Lead automated curation strategies—including smart pruning and downsampling—to minimize dataset bloat and maximize compute efficiency.\n Solve Long-Tail Challenges: Develop robust mining, training and evaluation pipelines for rare, safety-critical real-world scenarios.\n Innovate with Model Signals: Utilize uncertainty estimation, confidence scores, and embedding space analysis to uncover model blind spots and guide automated data acquisition.\n Collaborate Cross-Functionally: Work closely with researchers and operations teams to iterate on the end-to-end model development lifecycle.\n \n You Have: \n \n A bachelor’s degree in Machine Learning, Robotics, or Computer Science. 3+ years of professional experience in Machine Learning and/or Computer Vision.\n Proven, hands-on experience applying active learning in production environments.\n Strong expertise in building large-scale ML data pipelines (mining, extraction, auto-labeling, ingestion).\n Deep understanding of data curation—balancing, core set selection, and sampling—to optimize model performance.\n Proficiency in Python and deep learning frameworks (PyTorch or JAX).\n Strong software engineering skills for writing robust, production-ready code.\n \n We Prefer: \n \n An advanced degree (MS or PhD) in Machine Learning, Robotics, or Computer Science.\n A record of publications at top-tier conferences (e.g., CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, IROS, RSS, AAAI, IJCV, PAMI).\n Experience with C++\n Experience building data-centric infrastructure from the ground up to accelerate model iteration cycles.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["computer-vision","deep-learning","autonomous-vehicles","robotics","data-pipeline","pytorch","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8127006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-13T22:58:13Z","expires_at":"2026-09-29T13:35:05.310038Z","created_at":"2026-08-25T18:27:25.773269Z","updated_at":"2026-08-30T13:35:05.448509Z","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/c78452e5-7fd6-46e4-931b-4317dab14624"}],"page":1,"per_page":20,"total":548,"total_is_exact":true,"total_pages":28}