{"has_next":true,"jobs":[{"id":"0d7d0e83-e4e7-436c-8ea8-9998eef08a01","company_id":"75dcf7c0-5121-45f1-8d1b-6bfbfe15072f","title":"Helix AI Engineer, Android","slug":"helix-ai-engineer-android-4b9829bf","description":"Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose and require 5 days/week in-office collaboration. It’s time to build.\n We're looking for a Senior Android Engineer with deep expertise in low-level Android systems, the NDK, and real-time sensor and video pipelines. This is not a standard Android app role — you'll be building the mobile application that interfaces directly with our custom sensor hardware over USB, ingests high-frequency camera and IMU data in real time, and runs on-device AI inference at the edge.\n If you've spent time below the Java/Kotlin layer — writing C/C++ via the NDK, implementing custom HALs, or building zero-copy sensor pipelines — this role was built for you.\n WHAT YOU'LL DO \n \n Build and own the Android application that serves as the primary mobile interface to Figure's humanoid robots, connected via USB Host / Android Open Accessory protocols.\n Architect high-throughput, zero-drop data ingestion pipelines for high-FPS image sensors and high-frequency IMU data, using zero-copy memory techniques and real-time concurrency models.\n Implement custom hardware abstraction layers (HAL) and leverage the Android NDK (C/C++) for high-performance, low-latency processing.\n Optimize CPU/GPU workloads for real-time edge filtering under strict thermal and battery constraints, using foreground services and WorkManager for bulletproof background operation.\n Integrate on-device AI inference libraries (TFLite, MediaPipe, ONNX Runtime, OpenCV) for real-time computer vision and sensor fusion.\n Implement low-latency video streaming protocols (e.g. WebRTC) \n \n WHAT WE'RE LOOKING FOR \n \n Deep expertise in Android NDK (C/C++) — custom HAL development, USB Host/AOA protocol communication, and direct hardware interfacing below the standard SDK layer.\n Proven experience architecting real-time, low-latency data pipelines for high-bandwidth sensors — zero-copy memory, real-time concurrency, and synchronization with zero frame drops.\n Mastery of Android system resource management: CPU/GPU workload optimization, thermal and battery constraints, foreground services, and WorkManager.\n Strong proficiency in both C/C++ (NDK) and Kotlin/Java for Android.\n Experience shipping production Android applications in hardware-connected, latency-critical environments.\n Proven track record shipping and maintaining production Android applications at scale — including crash rate management, OTA update rollout strategies, real-time telemetry and monitoring pipelines, and sustaining reliability across a large, diverse active user base spanning multiple device configurations and Android OS versions\n \n NICE TO HAVE \n \n Experience integrating on-device CV/ML inference: TensorFlow Lite, MediaPipe, ONNX Runtime, or OpenCV applied to raw sensor feeds.\n Familiarity with WebRTC or other low-latency streaming protocols for real-time video.\n Background in DSP techniques applied directly to raw sensor data.\n Prior work in robotics companion apps, industrial Android devices, AR/computer vision mobile apps, automotive HMI, or drone control applications.\n \n The US base salary range for this full-time position is between $150,000 - $400,000 annually.\n The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.","salary_min":150000,"salary_max":400000,"location":"San Jose, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","robotics","tensorflow","computer-vision","mobile"],"apply_url":"https://job-boards.greenhouse.io/figureai/jobs/4685209006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T22:27:59Z","expires_at":"2026-06-29T14:05:53.434799Z","created_at":"2026-05-29T14:18:08.419145Z","updated_at":"2026-05-30T14:05:53.546009Z","company_name":"Figure AI","company_slug":"figure-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=figure.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0d7d0e83-e4e7-436c-8ea8-9998eef08a01"},{"id":"b2263952-2d61-4a59-acd2-4d8506c9b16e","company_id":"cf6855a4-6591-475f-be10-f3f36cf31758","title":"Senior Software Engineer, Search Relevance","slug":"senior-software-engineer-search-relevance-8f221ba2","description":"WHAT IS BOX?  \n Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.\n By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.\n WHY BOX NEEDS YOU \n The Search Relevance team at Box powers discovery across billions of files, enabling customers to find the right content quickly, securely, and intelligently. As we expand into a new era of AI-powered content understanding, we’re investing in the foundation that makes great search possible: reliable systems, strong signals, and models that learn from real-world usage.\n This is a rare opportunity to work at the intersection of information retrieval science, applied machine learning, and large-scale distributed systems. You’ll be building the infrastructure that powers intelligent content discovery for Fortune 500 companies—where milliseconds matter, relevance is measurable, and your experiments directly impact how millions of users work.\n We’re looking for a Senior Software Engineer to elevate search quality end-to-end—signals, ranking, retrieval, and evaluation—while building scalable, low-latency services that serve queries in real time. You’ll partner with Product, Data, and Infra teams to productionize cutting-edge models and experimentation frameworks, and help define the future of Box’s content intelligence, including hybrid and semantic search and our next-generation content agent.\n WHAT YOU'LL DO  \n \n Build and improve ranking, retrieval, and recommendation systems; identify the right signals and metrics to drive quality improvements that users can feel.\n Apply cutting-edge techniques (embeddings, LLM-enabled retrieval, hybrid search) to productionize experimentation and evaluation pipelines that scale to trillions of documents.\n Define and execute offline/online evaluation, A/B testing, and relevance tuning (NDCG, MRR, precision@k) to continuously improve search outcomes.\n Develop infrastructure for low-latency, high-availability query serving and near real-time indexing across distributed systems.\n Tackle distributed systems challenges including data sharding, intelligent routing, replication, and performance optimization.\n From ETL pipelines and feature engineering to model serving and result ranking—understand how data flows through the system and optimize at every stage.\n Lead design and implementation of new platform components from the ground up; establish patterns, raise the bar on code quality, and champion best practices.\n Share your expertise, contribute to technical direction, conduct thoughtful code reviews, and help shape our engineering culture.\n Participate in our on-call rotation, available at all times while on-call to help respond to and triage any issues that arise.\n \n WHO YOU ARE  \n \n 5+ years of industry experience building and operating backend or distributed systems at scale.\n Strong proficiency in an object-oriented language (e.g., Java, Scala, C++, or Python); Python experience strongly preferred.\n Hands-on experience building ranking, recommendation, NLP, or applied AI platforms in production. You understand the ML lifecycle from training to serving.\n Comfortable with data pipelines, message queues, and/or streaming systems (e.g., Kafka, Pub/Sub) and near real-time data processing.\n Experienced deploying and operating microservices in cloud environments; solid grasp of reliability, observability, and performance best practices.\n BS in Computer Science or related field, or equivalent practical experience.\n AI-first mindset—pragmatic about using the right models, signals, and evaluation methods to improve outcomes quickly and measurably.\n \n PREFERRED \n \n Experience with Elasticsearch, Solr, Lucene, or building custom search systems; deep understanding of inverted indexes, scoring functions, and query optimization.\n Knowledge of ML relevance tuning, learning-to-rank, retrieval evaluation metrics, offline/online testing, and A","salary_min":198500,"salary_max":248000,"location":"Redwood City, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["search","tensorflow","distributed-systems","pytorch","llm","nlp","fine-tuning","mlops"],"apply_url":"https://job-boards.greenhouse.io/boxinc/jobs/7926452","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T19:12:52Z","expires_at":"2026-06-29T14:19:20.83221Z","created_at":"2026-05-29T15:11:42.002134Z","updated_at":"2026-05-30T14:19:20.940887Z","company_name":"Box","company_slug":"box","company_logo_url":"https://www.google.com/s2/favicons?domain=box.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b2263952-2d61-4a59-acd2-4d8506c9b16e"},{"id":"88b5244c-2383-4f06-b5ef-0ade11296098","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Technical Lead Manager, Prediction \u0026 Planning, Machine Learning Eval","slug":"staff-technical-lead-manager-prediction-planning-ml-eval-29b43259","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently. \n We have an exciting opportunity for a Staff Technical Lead Manager to lead our ML Evaluation team. In this role, you will define the strategic vision for our evaluation platforms, scaling the critical infrastructure and metrics required, and partner closely with the modeling teams to rigorously validate our next-generation deep neural networks and accelerate ML developer velocity across PrePlan.\n You will: \n \n Influence the strategic direction of foundational infrastructure and evaluation platforms to robustly support next-generation ML model evaluation use cases\n Collaborate cross-functionally with ML engineers, data scientists, and infrastructure teams to identify, define, and surface critical signals on model, component, and system-level performance\n Leverage and scale evaluation and infrastructure platforms to significantly enhance the ML developer experience, enabling faster iteration through earlier, more reliable, and trusted model evaluation\n Manage and mentor a focused team of engineers, aligning their career growth and aspirations with critical organizational needs\n Drive best practices and leverage deep technical awareness of the Alphabet ML stack (e.g., TensorFlow, JAX, Flax, Apache Beam) to optimize evaluation workflows\n Stay at the forefront of emerging technologies, industry trends, and research in ML evaluation methodologies and advanced metrics design\n \n You have:  \n \n M.S. in Computer Science, Mathematics, or equivalent industry experience in Robotics or large-scale ML systems with critical evaluation needs\n 5+ years of experience building and maintaining large-scale distributed infrastructure, ML inference systems, or evaluation platforms, including 3+ years of engineering management experience\n Strong coding and testing proficiency, specifically in Python and C++\n Strong foundational knowledge of model evaluation and core data science principles (e.g., confidence intervals, outlier identification, curve fitting, and causality analysis)\n Familiarity with large-scale ML deployment and orchestration tools (e.g., TF Serving, TorchServe, Kubeflow, SageMaker Pipelines, or Vertex AI Pipelines)\n Understanding of machine learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or TensorFlow\n \n We prefer: \n \n Experience developing and maintaining evaluation pipelines for ML models\n Experience deploying and supporting machine learning models for computer vision, natural language processing, robotics/motion planning, or recommendation systems\n Experience supporting a small team of MLEs developing high-capacity, production-grade models and components\n Strong understanding of metrics computation and regression detection at scale\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":"onsite","job_type":"full-time","experience_level":"lead","tags":["robotics","nlp","computer-vision","pytorch","autonomous-vehicles","deep-learning","tensorflow","evaluation"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7963516","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T17:26:50Z","expires_at":"2026-06-29T14:04:32.30248Z","created_at":"2026-05-29T14:12:24.077985Z","updated_at":"2026-05-30T14:04:32.417838Z","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/88b5244c-2383-4f06-b5ef-0ade11296098"},{"id":"2f82717a-ca5c-44ec-afbc-871db9888784","company_id":"f36ec848-cb19-4b95-a680-6733e58086c0","title":"Director, Data Science","slug":"director-data-science-e0c2bfe0","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 Job Summary \n May Mobility is entering an exciting phase of growth as we expand our autonomous transit and mobility services across the country. Founded in 2017 by a team of experienced roboticists, perception, behavior, AI, and software engineers, we operate driverless transit shuttles in real communities — not as a research demonstration, but as a daily-service product that people rely on to get to work, school, and home.\n The Director, Data Science will lead the team responsible for turning the data generated by our fleet, simulation environment, and ML systems into the insights, evaluations, and decisions that make our autonomous service safer, more efficient, and ready to scale into new cities. You will own data science across simulation and synthetic data, perception and planning ML evaluation, fleet operations analytics, and the data infrastructure that supports them. You will partner directly with Engineering, Product, Operations, and Safety leadership to set measurement standards, define release criteria, and translate frontline operating data into the next generation of our autonomy stack.\n This is a leadership role for someone who has scaled a data science function inside a hard-tech environment, who is comfortable making engineering and product tradeoffs alongside their team, and who sees the gap between research-grade ML and production transit-grade ML as the most interesting problem in the industry today.\n Essential Responsibilities \n \n Set and own the data science strategy across simulation and synthetic data, ML evaluation (perception, prediction, planning), fleet operations analytics, and the data platform that supports them; translate that strategy into a 12–24 month roadmap with measurable milestones.\n Lead, grow, and develop a team of senior data scientists, ML engineers, and front-line managers; recruit from a small expert pool, calibrate the bar, and build a hiring brand that allows May Mobility to win against AV, robotics, and AI competitors.\n Partner with Engineering, Product, Safety, and Operations leaders to define release criteria, performance metrics, and ODD-expansion gates; use data to make the business case for what we deploy, where, and when.\n Drive ML and analytics applications end-to-end: dataset curation, scenario coverage, modeling, offboard evaluation, productionization, and continuous monitoring of fleet performance in the wild.\n Establish measurement and experimentation standards across the company — including before/after analyses for stack changes, A/B-style comparisons in simulation, and statistically credible reporting on real-world incidents.\n Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for production data science systems.\n Track and trend technical performance of the autonomy stack in the field; surface root causes, prioritize fixes with engineering, and represent fleet-data findings to executives, regulators, and partners.\n Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the ML changes most likely to move our key safety and service metrics.\n Represent May Mobility's data science work externally where appropriate — through publications, conference talks, partner reviews, and recruiting.\n \n Skills and Abilities \n Success in this role typically requires the following competencies: \n \n Autonomy Data Expertise. Can reason fluently about the data produced by a modern AV stack — sensor logs, perception outputs, planning traces, simulator results, and operational telemetry — and can identify which signals matter for which decisions.\n Hands-On Technical Depth. Has personally shipped production ML or analytics systems within the last 3–5 years and is credible in code review and design review with senior engineers and scientists.\n Cross-Functional Translator. Can explain a complex ML or statistical finding to engineering, product, and executive audiences; and ","salary_min":217000,"salary_max":312000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["robotics","healthcare","distributed-systems","pytorch","computer-vision","tensorflow","reinforcement-learning","autonomous-vehicles"],"apply_url":"https://job-boards.greenhouse.io/maymobility/jobs/8561428002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T21:54:47Z","expires_at":"2026-06-29T14:17:06.3175Z","created_at":"2026-05-28T14:18:43.046233Z","updated_at":"2026-05-30T14:17:06.431533Z","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/2f82717a-ca5c-44ec-afbc-871db9888784"},{"id":"23d134c7-f2bf-4c83-87f8-3938851bc707","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-583097ba","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n ABOUT THE TEAM\n Anduril's Air \u0026 Missile Defense Radar team develops cutting-edge tracking algorithms and software systems that detect, track, and characterize airborne threats in real-time. We're building the next generation of tracking intelligence capabilities—automated analysis systems that understand tracking performance, identify failure modes, and continuously improve our algorithms through data-driven insights.\n This role sits at the intersection of ML engineering and tracking domain expertise. You'll build end-to-end pipelines that ingest tracking algorithm telemetry, analyze correlation failures and performance anomalies, train models to automate root cause analysis, and deploy production tools that help engineers ask questions like \"why didn't track X and track Y associate?\" We don't just track targets; we track our tracking systems and make them smarter.\n WHAT YOU'LL DO\n \n Own tracking intelligence infrastructure end-to-end : Build the platform for ingesting tracking algorithm telemetry (hypotheses, scores, gains, association decisions), feature engineering performance metrics, training analysis models, and deploying them into production\n Automate tracking analysis : Develop ML models that identify correlation failures, track quality degradation, and root causes for tracking anomalies—replacing manual deep-dive investigations with scalable automated insights\n Build autotuning capabilities : Create systems that recognize incoming data characteristics and automatically adjust tracking algorithm parameters, frame rates, and model configurations for optimal performance\n Design human-in-the-loop tools : Build interfaces and query services that let engineers ask natural questions about tracking behavior and get data-driven answers backed by your models\n Exploit tracking telemetry : Instrument C++ tracking algorithms with appropriate logging (working with platform engineers), then marshal that data into consistent formats for analysis and model training\n Deploy in constrained environments : Package and deploy models for air-gapped systems with no external connectivity, following security scanning requirements where ML models are treated as data artifacts\n Manage the ML lifecycle : Handle data catalogs, ground truth labeling, model registries, versioning, and validation—ensuring models improve tracking performance in measurable ways\n Bridge domains : Translate between tracking algorithm fundamentals (Kalman filters, data association, multi-hypothesis tracking) and ML/data science techniques to build solutions that actually work\n Drive make/build decisions : Evaluate when to build custom models vs. leverage existing ML capabilities, selecting appropriate algorithm architectures for tracking intelligence problems\n Work hands-on-keyboard : This is a one-person show initially—you'll architect, code, deploy, and iterate rapidly using modern Python-based ML tooling\n \n REQUIRED QUALIFICATIONS\n \n 3+ years of experience with a strong mix of ML engineering and data science—you've built models AND deployed them into production systems\n Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)\n Experience with MLOps practices: data pipelines, feature engineering, model versioning, experiment tracking, and deployment workflows\n Familiarity with ML infrastructure tooling (MLflow, Dagster/Airflow, or similar orchestration tools)\n Understanding of tracking, estimation, or filtering algorithms (Kalman filters, data association techniques)—you need to understand what tracking algorithms output and why they make the decisions they do\n Ability to work with streaming time-series data and engineer features from algorithm telemetry\n Experience building data catalogs, managing ground truth labels, and validating model performance\n Strong software engineering fundamentals—you can build maintainable, production-quality code independently\n Comfortable working in C++ environments enough to add instrumentation/logging (no deep algorithm development required)\n Ability to obtain and maintain a U.S. Top Secret SCI security clearance\n \n PREFERRED QUALIFICATIONS\n \n Experience deploying ML models in edge, embedded, or air-gapped environments with security constraints\n Background in def","salary_min":165000,"salary_max":218000,"location":"Fort Collins, CO","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["tensorflow","mlops","computer-vision","data-pipeline","pytorch","payments","machine-learning"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5126634007?gh_jid=5126634007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T21:29:38Z","expires_at":"2026-06-29T14:06:48.665653Z","created_at":"2026-05-28T14:08:23.033047Z","updated_at":"2026-05-30T14:06:48.786007Z","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/23d134c7-f2bf-4c83-87f8-3938851bc707"},{"id":"41b3afd9-e8d0-4d82-9e8e-9149ad7c9147","company_id":"0bedcaf4-210e-4f52-95d5-a82be8aff446","title":"Sr Machine Learning Engineer, AI Research","slug":"sr-machine-learning-engineer-ai-research-866a2680","description":"Join the company that’s building the telemetry infrastructure for the AI era. At Cribl, we partner with IT and Security teams at many of the world’s biggest enterprises, including half of the Fortune 100, to bridge the gap between AI ambition and infrastructure reality. As the AI Platform for Telemetry, we give customers the choice, control, and flexibility to manage and analyze telemetry for both humans and agents, so they can build what’s next.\n We’re one of the fastest‑growing private companies and a leading player in a massive, fast‑moving market. With a global workforce, we’re remote‑first and grounded in a simple idea: software is a people business. Cribl is the place where curious, collaborative people can do their best work, grow fast, and bring their full selves to the herd.\n Why You'll Love This Role \n You will work closely with the founding team and a group of highly-skilled engineers to shape the future of AI-enabled Security/Observability platforms. You will play a central role in bringing integrating cutting-edge AI/ML technologies to the Cribl Product suite to help solve real customer problems.  You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers.\n On top of it all you will have fun. \n Cribl strives to be a great place to work for everyone.\n As An Active Member Of Our Team, You Will... \n \n Design, train, and evaluate machine learning models across a range of research and applied AI initiatives\n Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements\n Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems\n Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation\n Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation\n Contribute to a culture of rigorous experimentation; tracking results, documenting findings, and sharing learnings with the broader team\n Stay current with the latest developments in ML and AI research, and proactively identify opportunities to apply them\n This position may require stand-by, on-call, or off-hours duties during critical research or deployment milestones\n \n If You've Got It - We Want It \n \n Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 4+ years of industry or research experience (Master's or PhD a plus)\n Deep hands-on experience training and evaluating ML models, including language models\n Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow\n Familiarity with MLOps tooling and infrastructure (e.g., MLflow, Weights \u0026 Biases, Kubeflow, or similar)\n Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques\n Strong ability to move fast without sacrificing rigor; you know when to prototype and when to productionize\n Excellent communication skills with the ability to clearly present experimental results to both technical and non-technical stakeholders\n \n #LI-Tag #LI-Remote\n The salary for this role is dependent on geographic location and will be based on the individual candidate's job-related knowledge, skills, and experience. In addition to base salary, for sales and some sales-adjacent roles, employees are eligible to earn incentive compensation (commission). For all other roles, employees are eligible to participate in the Cribl Corporate Bonus Program. In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), and equity.\n Base Salary Range\n $185,000 — $215,000 USD \n Bring Your Whole Self Diversity drives innovation, enables better decisions to support our customers, and inspires change for the better. We’re building a culture where differences are valued and welcomed, and we work together to bring out the best in each other. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which the candidate is applying. \n Interested in joining the Cribl herd? Learn more about the smartest, funniest, most passionate goats you’ll ever meet at cribl.io/about-us .","salary_min":185000,"salary_max":215000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["nlp","tensorflow","computer-vision","mlops","pytorch","reinforcement-learning","fine-tuning","research"],"apply_url":"https://cribl.io/job-detail/?gh_jid=5979543004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T18:02:31Z","expires_at":"2026-06-29T14:18:07.512926Z","created_at":"2026-05-28T14:19:42.491471Z","updated_at":"2026-05-30T14:18:07.623902Z","company_name":"Cribl","company_slug":"cribl","company_logo_url":"https://www.google.com/s2/favicons?domain=cribl.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/41b3afd9-e8d0-4d82-9e8e-9149ad7c9147"},{"id":"789228a9-72bb-4a56-98ca-87b1968a76fd","company_id":"698abc6f-9497-4ea6-809f-f0f7c2788a46","title":"Staff GPU Systems Engineer, Space Computing","slug":"staff-gpu-systems-engineer-space-computing-bf7c9dd7","description":"At Relativity Space, we’re building rockets to serve today’s needs and tomorrow’s breakthroughs. Our Terran R vehicle will deliver customer payloads to orbit, meeting the growing demand for launch capacity. But that’s just the start. Achieving commercial success with Terran R will unlock new opportunities to advance science, exploration, and innovation, pioneering progress that reaches beyond the known. \n Joining Relativity means becoming part of something where autonomy, ownership, and impact exist at every level. Here, you're not just executing tasks; you're solving problems that haven’t been solved before, helping develop a rocket, a factory, and a business from the ground up. Whether you’re in propulsion, manufacturing, software, avionics, or a corporate function, you’ll collaborate across teams, shape decisions, and see your work come to life in record time. Relativity is a place where creativity and technical rigor go hand in hand, and your voice will help define the stories we’re writing together. Now is a unique moment in time where it’s early enough to leave your mark on the product, the process, and the culture, but far enough along that Terran R is tangible and picking up momentum. The most meaningful work of your career is waiting. Join us. \n  \n About the Team:  \n The Interplanetary Sciences Program was established  to expand access to scientific exploration across our solar system. Its mission is to make planetary research faster, more affordable, and more capable than ever before by rethinking how science missions are designed, built, and  operated . The program aims to enable scientists to send instruments to distant worlds without decades of development or prohibitive costs. By creating a sustainable model for interplanetary exploration, we are transforming space science from an occasional event into a continuous process of discovery that accelerates knowledge, broadens participation, and inspires the next generation of explorers.   \n About the Role: \n \n Own the GPU compute environment for a space-based data center — setup, driver integration, container runtime, job scheduling, and performance optimization — building the platform that enables onboard AI/ML inference and SAR reprocessing millions of miles from the nearest sysadmin \n Profile and optimize compute performance across the full stack: GPU utilization, memory bandwidth, I/O throughput, and storage interface performance, squeezing maximum science return from constrained power and thermal budgets that shift between sunlit burst processing and eclipse idle periods \n Build power and thermal-aware compute scheduling that orchestrates batch workloads around orbital constraints, coordinating with the storage platform to sustain 10 Gbps data movement between NAS and compute nodes during processing windows \n Develop compute health monitoring and upset recovery mechanisms — checkpoint/restart strategies, GPU fault detection, and automated recovery — so a radiation-induced upset means a restarted job, not a lost processing window \n Integrate GPU drivers with the payload Linux image in coordination with the Platform RE, manage the container runtime for compute workloads, and ensure the platform reliably runs ML frameworks and SAR processing pipelines maintained by the broader operations team \n \n About You: \n \n BS/MS in Computer Science or Electrical Engineering and 5+ years of relevant experience \n Hands-on experience with GPU programming and compute frameworks — CUDA, ROCm, or OpenCL — with real performance profiling and optimization work, not just running tutorials \n Strong Linux systems administration and performance tuning skills: you've diagnosed I/O bottlenecks, tuned memory management, and understood why a workload isn't hitting expected throughput \n Experience with container technologies (Docker, Podman, or lightweight alternatives) and HPC job scheduling concepts \n Working proficiency in Python for tooling, scripting, and ML framework integration, with C/C++ skills for performance-critical system components \n \n Nice to haves but not required:    \n \n Experience with HPC cluster administration, ML infrastructure, or cloud GPU compute platforms at scale \n Deep familiarity with ML framework runtime requirements — PyTorch or TensorFlow deployment, model serving, and inference optimization \n Knowledge of GPU compute architectures at the hardware level: CUDA cores, compute units, memory hierarchies, and how they affect real workload performance \n Experience with high-throughput data movement and storage I/O optimization — NFS tuning, buffer management, and sustaining multi-gigabit throughput \n Background in power-managed computing: duty cycling, thermal throttling, and workload scheduling under variable power constraints \n Experience designing checkpoint/restart or fault-tolerant batch processing systems — space experience not required, similar problems exist in large-scale distributed infrast","salary_min":181000,"salary_max":248500,"location":"Long Beach, California","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["mlops","pytorch","fine-tuning","gpu","tensorflow"],"apply_url":"https://boards.greenhouse.io/relativity/jobs/8560518002?gh_jid=8560518002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T17:01:37Z","expires_at":"2026-06-29T14:18:13.388673Z","created_at":"2026-05-27T14:19:05.177476Z","updated_at":"2026-05-30T14:18:13.506801Z","company_name":"Relativity","company_slug":"relativity","company_logo_url":"https://www.google.com/s2/favicons?domain=relativity.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/789228a9-72bb-4a56-98ca-87b1968a76fd"},{"id":"30691bcd-dc37-4149-9f33-16fd4c446705","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Data Scientist, Guest Travel Insurance (Algorithms)","slug":"senior-data-scientist-guest-travel-insurance-algorithms-d5dcd08e","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 Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. Travel should feel safe—and AirCover is how we deliver on that promise. Through Guest Travel Insurance (GTI), we offer guests peace of mind at the moment of booking and throughout their trip. As a Data Scientist on AirCover, you’ll work at the intersection of insurance, personalization, and machine learning—building intelligent systems that help the right guest discover the right coverage at the right moment. You’ll join a tight-knit, high-output DS team that runs one of Airbnb’s most experiment-dense personalization roadmaps, partnering daily with product, engineering, operations, and legal to ship work that directly affects guest trust and revenue.\n The Difference You Will Make: \n We’re looking for a machine learning expert who is excited to own hard problems end-to-end—from prototype to production. You’ll have direct scope to contribute and lead across:\n \n Package personalization \u0026 ML-based recommendation: Evolve rule-based guest segmentation into a full ML recommendation system that surfaces the right insurance (e.g., trip cancellation, accidental damage coverage, on-trip protection) to each guest based on purchase intent, trip attributes, listing signals, and user history.\n Content personalization: Build models that rank and select benefit messaging for each guest—deciding which coverages to highlight, in what order, and with what framing—drawing on learnings from segmentation experiments and LLM-assisted content prototyping.\n Intent modeling: Develop and productionize ML models (from gradient-boosted trees to deep learning) that predict a guest’s likelihood to value specific coverages, using structured booking data and unstructured signals.\n Journey understanding and optimization: Leverage reinforcement learning to personalize across user journey, with understanding on user preferences on entry point, price, notification frequency, and trip characteristics\n High-velocity experimentation: Design and run adaptive experiments to maximize learning within tight traffic constraints; sequence ERFs strategically to keep the personalization roadmap moving.\n \n A Typical Day: \n \n Dig into experiment results to surface high-impact personalization opportunities; translate what you find into crisp scientific problem formulations that balance rigor with speed-to-learning.\n Work closely with product managers, engineers, operations, legal, and privacy partners to align on ML requirements, de-risk design decisions, and gather requirements on explainability and compliance.\n Hands-on develop, evaluate, and ship ML models and data pipelines at scale—batch and real-time, structured and unstructured—using Airbnb’s paved-path tooling and AI native mindset\n Prototype and iterate quickly: turn a new idea into a working model in a prototype, get early signals from an experiment, then productionize what works. You move fast and don’t wait to be asked.\n Present findings and proposals at team reviews and to technical, product, and executive stakeholders—making complex ML results legible without dumbing them down, and generating conviction on the roadmap ahead.\n Stay current with the research community; draw on state-of-the-art advances in recommendation systems, LLMs, and personalization to raise the bar for what the team ships. Occasionally publish externally or present at conferences to advance Airbnb’s scientific standing.\n \n Your Expertise: \n \n 5+ years of relevant industry experience (e.g., ML scientist, tech lead, junior faculty) and a Master’s degree or PhD with 2+ yrs in a relevant field.\n Proven hands-on experience building and shipping personalization and recommendation systems at scale: strong intuition for feature engineering, user modeling, and the full ML lifecycle (training, serving, monitoring, iteration). Experience with LLMs, Computer Vision or content-understanding topics is a strong plus.\n Strong fluency in Python and SQL; hands-on experience with TensorFlow or PyTorch, Airflow, and a data warehouse environment.\n Deep understanding of ML algorithms (gradient-boosted trees, deep learning, optimization) and experiment design—including A/B testing, multi-armed bandits, and the practical constraints of running experiments at scale. Causal inference skills are a plus.\n Exceptional communicator: you can make complex ML work legible to engineers, product managers, legal, and executives alike— written and verbal. You treat communication as a core part of the job, not an afterthought.\n Self","salary_min":179000,"salary_max":210000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["tensorflow","llm","deep-learning","pytorch","data-pipeline","computer-vision","reinforcement-learning","data-science"],"apply_url":"https://careers.airbnb.com/positions/7926614?gh_jid=7926614","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T15:55:19Z","expires_at":"2026-06-29T14:09:02.248406Z","created_at":"2026-05-27T14:09:19.322462Z","updated_at":"2026-05-30T14:09:02.357735Z","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/30691bcd-dc37-4149-9f33-16fd4c446705"},{"id":"3261877c-be75-4d43-85d6-3e4911c5fc14","company_id":"5fac52d7-9b0b-4990-80a2-e2949dd0af1d","title":"Senior Software Engineer, ML/AI Platform","slug":"senior-software-engineer-mlai-platform-80ff4bf6","description":"Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.\n  \n Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.\n  \n With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in  Deloitte’s Fast 500  (four years running!),  LinkedIn’s Top Startups ,  Forbes’ Cloud 100 (five years running!),  Inc.’s Best Workplaces , and the  Human Rights Campaign Foundation's Corporate Equality Index !\n About the Role We’re looking for a self-motivated, highly driven Senior Software Engineer to join our Machine Learning Platform  (MLPlatform) team. As a team, we enable Attentive’s Machine Learning (ML) practice to directly impact Attentive’s AI product suite through the tools to train, serve, and deploy ML models with higher velocity and performance, while maintaining reliability. We build and maintain a foundational ML platform that spans the full ML lifecycle for use by ML engineers and data scientists. This is an exciting opportunity to join a rapidly growing ML Platform team at the ground floor, with the ability to drive and influence the architectural roadmap, enabling the entire ML organization at Attentive. This team and role are responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform. We are excited to bring on more engineers to continue expanding this stack.\n What You’ll Accomplish \n \n Unlock offline \u0026 real-time access to trillions of data points for our ML and Data Science teams.\n Manage, expand, and optimize our feature store that enables feature engineering, multi-TB scale training jobs, and offline / real-time inferencing.\n Support PB scale data operations on the feature store using Apache Spark, Spark Structured Streaming, Kafka, and Ray.\n Partner with other teams and business stakeholders to deliver ML and AI initiatives.\n \n Your Expertise \n \n You have been working in the areas of Data Engineering / MLOps for 5+ years, and have built and matured the pipelines of a PB-scale feature store.\n You have deep Apache Spark, Spark Streaming, and Ray Data experience and built data pipelines for ML use cases using these tools.\n You understand the correlation between data cardinality, query plans, configuration settings, and hardware and the impact of each on data pipeline performance .\n You know/have created infrastructure for Training ML models/fine-tuning LLMs. \n You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs.\n \n What We Use \n \n Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS.\n Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm.\n Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS.\n Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright.\n Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas.\n \n You'll get competitive  perks and benefits , from health \u0026 wellness to equity, to help you bring your best self to work.\n For US based applicants: \n \n The US base salary range for this full-time position is $180,000 - $250,000 annually   + equity + benefits\n Our salary ranges are determined by role, level and location\n \n #LI-EZ1   \n By applying for this position, your data will be processed as per Attentive's Privacy Policy . \n Attentive Company Values \n Default to Action - Move swiftly and with purpose\n Be One Unstoppable Team - Rally as each other’s champions\n Champion the Customer - Our success is defined by our customers' success\n Act Like an Owner  - Take responsibility for Attentive’s success\n  \n Learn more about AWAKE , Attentive’s collective of employee resource groups.\n  \n If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches you","salary_min":180000,"salary_max":250000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["api-design","data-pipeline","pytorch","fine-tuning","tensorflow","microservices","llm","mlops"],"apply_url":"https://job-boards.greenhouse.io/attentive/jobs/4252255009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T20:47:29Z","expires_at":"2026-06-29T14:18:28.156485Z","created_at":"2026-05-27T14:19:20.116375Z","updated_at":"2026-05-30T14:18:28.272305Z","company_name":"Attentive","company_slug":"attentive","company_logo_url":"https://www.google.com/s2/favicons?domain=attentive.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3261877c-be75-4d43-85d6-3e4911c5fc14"},{"id":"93fe1130-903b-482a-9d72-9c439f5cead5","company_id":"0d5cf132-03f2-47aa-8161-dcb1b95aca68","title":"Software Product Manager","slug":"software-product-manager-96ae9837","description":"What MatX Is Building \n MatX is on a mission to make the world's best AI models run as efficiently as possible, accelerating global progress in AI quality and accessibility. We are building cutting-edge AI infrastructure at the intersection of hardware and software, and our team is growing quickly.  \n This is MatX's first software-focused Software Product Manager. You'll start by doing mostly internal work that sits at the boundary of product management and technical program management: mapping interfaces between sub-teams, driving API boundary decisions, and making sure the compiler, simulator, kernels, and runtime teams are building toward a coherent whole. You will be managing risks and mitigations applying structural problem solving methodology as needed. You'll also be shaping the software side of our product definition — what goes into the SDK, what tools and documentation we ship, and what kernels and systems software we build or expose to partners.  \n What You'll Do Here \n \n Partner Management: Own the SDK partner relationship model — which partners get early access, what commitments we make, and how we collect and triage feedback \n API \u0026 Systems Definition: Define the shape of the MatX SDK and the systems software surface area we ship to customers: runtime APIs, profiling tools, compiler interfaces, and simulator integrations Own and document API boundaries between the compiler, simulator, kernels, and runtime sub-teams, and across hardware, architecture, and systems software — driving alignment on interface contracts, timelines, and dependencies \n \n \n Kernel Strategy: Determine what kernels MatX should author versus expose for customers to write themselves, and define the tools and documentation that make customer kernel authorship tractable \n Technical Translation: Translate between customer needs and engineering constraints, and contribute to roadmap prioritization across the software stack with a clear view of customer impact and strategic fit \n \n Who You Are \n \n Experience in product management or technical program management, working directly on systems software, compilers, ML frameworks, or developer-facing SDKs \n Strong enough technical depth to read a Rust API, understand what a compiler IR is, and have a meaningful conversation with an engineer about simulator fidelity tradeoffs — you don't need to write production code, but you need to earn trust with people who do  \n Experience defining and documenting API contracts or SDK specifications in a technical organization  \n Comfort operating in ambiguity and doing the unglamorous internal coordination work before the external product work becomes available  \n Strong written communication — you'll write a lot of internal specs, interface docs, and partner-facing materials  \n \n Bonus Points If You Have \n \n Background in ML accelerator software (inference runtimes, kernel libraries, compiler backends, or ML frameworks such as PyTorch, JAX, or TensorFlow at the systems level) \n Experience working with early SDK partners or developer ecosystem programs at a hardware or developer-tools company  \n Familiarity with CUDA/HIP, MLIR, LLVM, or similar low-level toolchains — not necessarily as an author, but as someone who has shipped documentation or tooling around them \n Previous experience at a company that shipped novel hardware with a co-designed software stack (GPU vendors, custom accelerator startups, or similar)  \n Previously a software engineer \n \n Compensation \n The US base salary for this full-time position is determined based on a variety of factors including role, experience, location, job related skills, and relevant education and training. Career length is only a guideline for compensation.  \n \n Early Career - $120,000 - $275,000 + equity \n Mid Career - $175,000 - $400,000 + equity \n Senior Career - $250,000 - $600,000 + equity \n \n What We Offer \n \n A Stake in our success  A cash/equity mix that fits your needs and option to do early exercise \n Health \u0026 Wellness  Company subsidized Health, Dental, Vision, and Life insurance; Pre-tax Health Savings Accounts with generous company contribution (even if you don’t) \n Time To Recharge  4 weeks paid time off (accrued), 12 company holidays, and 3 weeks remote/flexible work per year \n Support to Parents  Up to 12 weeks of paid parental leave, regardless of your path to parenthood \n Learning \u0026 Development  $1,500 yearly towards your professional development e.g. conferences, courses, and other learning opportunities \n Team Connection  Team Lunches, quarterly off-sites, and regular town halls \n Financial Wellbeing  401K and/or Roth IRA, with 5% company contribution, even if you don’t! \n Flexible Spending Accounts  Pre-tax spend accounts for medical, dental/vision, dependent care, parking, and transit expenses \n Commute On Us  For those commuting up to 1 hour, put your rideshare cost on our company card and reclaim the drive-time to get work done! \n MatX E[x]tras  $50","salary_min":250000,"salary_max":600000,"location":"Mountain View, CA","workplace":"remote","job_type":"full-time","experience_level":"principal","tags":["pytorch","gpu","tensorflow","cloud"],"apply_url":"https://job-boards.greenhouse.io/matx/jobs/5225344008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T17:29:08Z","expires_at":"2026-06-29T14:13:29.128301Z","created_at":"2026-05-27T14:14:02.696883Z","updated_at":"2026-05-30T14:13:29.241055Z","company_name":"MatX","company_slug":"matx","company_logo_url":"https://www.google.com/s2/favicons?domain=matx.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/93fe1130-903b-482a-9d72-9c439f5cead5"},{"id":"1ba3f061-ae42-40e0-ac56-4971d990cc25","company_id":"64071af6-a121-45a1-8ec4-2bada031ba1f","title":"Principal Security Engineer, Product \u0026 AI","slug":"principal-security-engineer-product-ai-ffd6eb12","description":"As Marqeta’s Principal Security Engineer you will serve as the technical lead across our security engineering function. This role combines three critical responsibilities: leading product security engineering across our payment platform, building our AI security program as we scale generative AI and ML capabilities, and providing security architecture oversight across enterprise and infrastructure security.\n Your primary focus will be product security and AI—threat modeling payment features, securing APIs, building genAI controls, and ensuring AI-powered capabilities ship securely. You'll also own the security architecture function and provide technical oversight for infrastructure security—endpoint protection, network security, VPN, and enterprise security controls—ensuring coherent security standards across everything we build and operate.\n You'll partner closely with Product Security, Infrastructure Security, and Security Operations teams and serve as the security voice in our Model Risk Office. This is an individual contributor role with mentoring responsibilities and broad technical influence across the security, engineering, and business technology organizations.\n We work Flexible First . This role can be performed remotely anywhere within the United States or from our Oakland office. We’d love for you to join us!\n You'll have the chance to:\n \n Lead product security engineering for our payment platform—owning threat modeling, security architecture review, secure SDLC practices, and API security across the engineering organization\n Help mature our AI security programdeveloping genAI controls, securing ML pipelines, and working alongside the Model Risk Office for model evaluations.\n Provide security architecture oversight across infrastructure and enterprise security—endpoint, network, VPN, and corporate security controls—ensuring technical standards are coherent across all security domains\n Shape how security engineering scales across the organization through tooling, frameworks, security champions engagement, and engineering partnerships\n \n The Impact You'll Have: \n Product Security:\n \n Conduct security architecture reviews and threat modeling for new product features, APIs, and service integrations across the payment platform\n Define and maintain secure development lifecycle practices including secure code review standards, API security patterns, and authentication/authorization frameworks\n Develop self-service security tooling and developer-facing guardrails that reduce friction while maintaining security posture\n \n AI Security:\n \n Lead security strategy and risk assessment for AI/ML systems including customer-facing AI products, fraud detection models, LLM integrations, and recommendation systems\n Build genAI security controls—prompt injection prevention, output filtering, model validation, and monitoring frameworks\n Perform security assessments of AI/ML model architectures, training pipelines, inference endpoints, and deployment infrastructure\n Evaluate and operationalize AI-powered security tools (e.g., AI-assisted code review, anomaly detection, automated threat intelligence) to improve security operations\n \n Enterprise \u0026 Infrastructure Security Oversight:\n \n Provide technical oversight for infrastructure security including endpoint protection, network security, VPN, and enterprise security controls\n Ensure coherent security architecture standards across product, cloud infrastructure, and corporate environments\n Drive technical decisions for security tooling and controls that span the full environment—from developer laptops to production infrastructure\n \n Across All Domains:\n \n Partner across Product Security, Infrastructure Security, and Security Operations teams as well as engineering, data science, and compliance\n Mentor security engineers and cross-functional teams, raising the organization's overall security engineering maturity\n Communicate security risks and strategy to executive and board-level audiences\n \n Who You Are: \n \n 10+ years of security engineering experience with demonstrated technical leadership across multiple security domains; or equivalent combination of education and experience\n Deep product security expertise: threat modeling, security architecture review, secure code review, API security, authentication/authorization design, and secure SDLC practices\n Experience with or strong interest in AI/ML security—understanding of risks including adversarial attacks, model poisoning, prompt injection, data privacy, and AI supply chain threats. We want someone who is genuinely excited about AI technology and wants to secure it, not just govern it\n Broad security fluency across infrastructure and enterprise security—endpoint protection, network security, identity, and cloud security—even if your deepest expertise is in application and product security\n Experience working in cloud-native environments (AWS preferred) with familiarity across AI/ML services (Bed","salary_min":256800,"salary_max":321000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"principal","tags":["llm","security","payments","rag","pytorch","tensorflow","cloud","generative-ai"],"apply_url":"https://job-boards.greenhouse.io/marqeta/jobs/7868953","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-14T21:10:44Z","expires_at":"2026-06-29T14:09:37.336666Z","created_at":"2026-05-15T14:10:42.844043Z","updated_at":"2026-05-30T14:09:37.448483Z","company_name":"Marqeta","company_slug":"marqeta","company_logo_url":"https://www.google.com/s2/favicons?domain=marqeta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1ba3f061-ae42-40e0-ac56-4971d990cc25"},{"id":"e6e02d48-64a2-4987-873c-febe6c9cee5e","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Sr. Principal / Distinguished ML Scientist, Autonomous Science for Cell Biology","slug":"sr-principal-distinguished-ml-scientist-autonomous-science-for-cell-biology-d99f2c0e","description":"Your Impact at LILA \n Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), we are launching a new AI for Cell Biology team to develop autonomous-science capabilities for cellular and tissue biology; spanning single-cell omics, perturbation biology, spatial profiling, imaging, genetics, and multi-modal experimental data that integrate deep biological expertise with foundation modeling and agentic systems.\n We are seeking a Sr. Principal or Distinguished ML Scientist to be the founding senior ML Scientist on this team . This is a 0→1 leadership-grade role with a clear, complementary partnership at the top of the team. You will co-develop the team's scientific direction with the VP of AI for Cell Biology , and you will own the integration of cell-biology research with Lila's central autonomous-science platform — the foundation-model, agentic-systems, and experimental-automation infrastructure that closes the loop between AI reasoning and the lab. Where the VP carries cross-functional implementation (applications, commercial activities, and the operating interfaces with our autonomous-lab and product teams), you carry the technical architecture by which cell-biology research becomes part of Lila's broader autonomous-science capability.\n Cell- and tissue-scale biology sits at an open frontier of AI for science. The field has produced strong specialist models across sub-domains — single-cell foundation models, structural prediction, perturbation response, cellular imaging, pathway and ligand–receptor inference — but the architecture for system-level reasoning that ties these together, grounds them in experimental reality, and produces actionable mechanism-of-action hypotheses is still being defined. We have a working point of view on that architecture and on how Lila's autonomous-science platform extends to cellular biology; you will refine, challenge, or replace it. The architectural choices you make alongside the VP will shape what Lab-in-the-Loop autonomous science looks like at cell and tissue scale.\n This is a senior role for someone operating at the frontier of generative AI applied to biology, with the scientific judgment to define research strategy and the technical depth to drive end-to-end the architecture that integrates cell-biology research with our autonomous-science platform.\n What You'll Be Building \n \n Co-develop the scientific direction. Partner with the VP to define the cell-biology research agenda end-to-end — from problem formulation through architecture, large-scale training, evaluation, and integration into Lila's Lab-in-the-Loop autonomous-science lifecycle.\n Own integration with Lila's central autonomous-science platform. Architect how cell-biology research feeds into and benefits from Lila's foundation-model, agentic-systems, and experimental-automation infrastructure. This includes the cross-program inference architecture; the data, reasoning-trace, and experimental-protocol specifications shared with our central AI Research and autonomous-lab teams; the contribution path of cell-biology research into Lila's broader autonomous-science capability; and the working partnerships with those central teams that make integration coherent.\n Lead the foundation-model and integration architecture. Own the technical choices that turn cell-biology data into mechanism-grounded scientific inference — including which models to train or adapt at scale, which strong specialists to integrate from the field (single-cell foundation models, structural prediction, perturbation, spatial, imaging, pathway), and how to compose them into end-to-end reasoning systems. The design space is open and the architectural bet is yours to shape with the VP.\n Lead agentic discovery. Build systems that plan, execute, and reason over scientific experiments — closing the loop between models, our autonomous experimental platform, and wet-lab feedback to accelerate cellular and tissue biology research.\n Own the cross-program evaluation architecture. Design and steward the benchmarks and evaluation methodology that gauge progress across the team's cell-biology research programs and that show how those gains accrue to Lila's broader autonomous-science capability over time. Benchmarks you stand up here outlive any single program and become part of Lila's standing scientific evaluation suite.\n Translate between biology and ML. Frame complex cellular, multi-cellular, and tissue-scale biology questions as well-defined ML problems, and interpret model outputs alongside experimental scientists and computational biologists.\n Carry the scientific narrative. Internally, set technical standards for scientific excellence, reproducibility, and rigorous benchmarking, and grow scientific coherence across the team's research programs. Externally, represent Lila's AI for Cell Biology research through publications, talks, and engagement at premier sc","salary_min":360000,"salary_max":570000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["agents","distributed-systems","pytorch","tensorflow","generative-ai"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4246315009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-13T16:28:36Z","expires_at":"2026-06-29T14:17:44.224545Z","created_at":"2026-05-14T14:18:45.582157Z","updated_at":"2026-05-30T14:17:44.341609Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e6e02d48-64a2-4987-873c-febe6c9cee5e"},{"id":"a9111236-fbda-4441-bf24-4637736aca19","company_id":"5fac52d7-9b0b-4990-80a2-e2949dd0af1d","title":"Staff Software Engineer, Machine Learning ","slug":"staff-software-engineer-machine-learning-32926214","description":"Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.\n  \n Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.\n  \n With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in  Deloitte’s Fast 500  (four years running!),  LinkedIn’s Top Startups ,  Forbes’ Cloud 100 (five years running!),  Inc.’s Best Workplaces , and the  Human Rights Campaign Foundation's Corporate Equality Index !\n About the Role \n Our Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. As a Senior Machine Learning Engineer, you will play a critical role in building, scaling, and operating production-grade ML systems that drive real-time personalization across the Attentive platform.\n You will operate with a high degree of ownership, partner closely with Product and Engineering, and help raise the technical bar across our ML systems in a fast-paced, high-impact environment.\n What You’ll Accomplish \n \n You have a proven track record of building systems that maintain a high bar of quality\n You deeply loathe regressions and take proactive steps to protect against them through a variety of testing techniques\n You are a collaborator, technical leader, and a great communicator\n You are constantly improving the quality of the project you are working on, both via direct contributions as well as long-term advocacy for larger-scale changes\n You are enthusiastic about the high impact, fast-paced work environment of an late-stage startup\n 10+ years experience is ideal\n \n Your Expertise \n \n You have worked professionally building systems for 6+ years with experience on a single system long enough to see the consequences of your decisions\n Experience with TensorFlow/Pytorch, xgboost, pandas, matplotlib, SQL, Spark or similar tools\n You have proficiency or experience with Python\n You have extensive experience using machine learning and data analysis, or similar, to build scalable systems and data-driven products, working with cross-functional teams\n You have a proven track record of building scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation from modern research\n You have led cross-functional machine learning projects across teams\n \n What We Use \n \n Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS\n Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm\n Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS\n Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright\n Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas\n \n You'll get competitive  perks and benefits , from health \u0026 wellness to equity, to help you bring your best self to work.\n For US based applicants: \n \n The US base salary range for this full-time position is $320,000 - $360,000 annually   + equity + benefits\n Our salary ranges are determined by role, level and location\n \n #LI-EF1 \n By applying for this position, your data will be processed as per Attentive's Privacy Policy . \n Attentive Company Values \n Default to Action - Move swiftly and with purpose\n Be One Unstoppable Team - Rally as each other’s champions\n Champion the Customer - Our success is defined by our customers' success\n Act Like an Owner  - Take responsibility for Attentive’s success\n  \n Learn more about AWAKE , Attentive’s collective of employee resource groups.\n  \n If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches your skills and experience.\n  \n At Attentive, we know that our Company's strength lies in the diversity of our employees. Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. Our policy is to provide eq","salary_min":320000,"salary_max":360000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["api-design","pytorch","tensorflow","microservices","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/attentive/jobs/4244729009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-12T15:46:41Z","expires_at":"2026-06-29T14:18:28.482547Z","created_at":"2026-05-14T14:19:30.509909Z","updated_at":"2026-05-30T14:18:28.593409Z","company_name":"Attentive","company_slug":"attentive","company_logo_url":"https://www.google.com/s2/favicons?domain=attentive.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a9111236-fbda-4441-bf24-4637736aca19"},{"id":"e18fc3cf-eb2b-4bd1-ad59-c995ac686cde","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Sr. Software Engineer, Marketplace ML Platform","slug":"software-engineer-marketplace-ml-platform-7feb9e13","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 Marketplace team builds the core decision systems that keep Waymo running smoothly. They design the algorithms that match riders to vehicles, optimize routing, balance supply and demand, forecast rider demand, and power dynamic pricing. Their work ensures the fleet stays efficient, riders get fast pickups, and the AV network operates safely and profitably across cities.\n The Platform team is part of the Marketplace engineering: we provide specialized business platforms supporting Marketplace ML and Optimization products. We own and host core ML models used throughout the Marketplace; we automate all parts of the model life cycle, such as feature engineering,  training workflows and inference services; we are working on next-generation Marketplace Simulation system capable to run high-fidelity virtual experiments; we also streamline diverse Marketplace Experimentation practices with the goal of providing unified user-friendly Experimentation platform.\n You will: \n \n Implement and scale the core economic engine for Waymo's ride-hailing commercial services.\n Develop high-level infrastructure code for automatically training and deploying sophisticated optimization and prediction ML models to make real-time decisions for pricing, matching, and positioning.\n \n  \n You have: \n \n BS degree in Computer Science or equivalent practical experience.\n 4+ years of experience programming in backend coding languages such as Java or C++.\n Experience in building backend platforms supporting multiple product use-cases/services. \n Prior Machine Learning Engineering experience in Python using mature ML frameworks such as TensorFlow, PyTorch or Keras.\n \n  \n We prefer: \n \n MS in Computer Science, or equivalent practical experience.\n Experience building and deploying ML / Optimization models into production environments.\n Experience developing ML data pipelines and ML workflow automation code on top of a mature ML infra. \n Experience working at another Ride hailing or Marketplace company.\n Coursework background in ML and Optimization.\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 $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","data-pipeline","tensorflow","pytorch"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7905693","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-12T04:19:28Z","expires_at":"2026-06-29T14:04:30.394353Z","created_at":"2026-05-12T14:05:22.694422Z","updated_at":"2026-05-30T14:04:30.504243Z","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/e18fc3cf-eb2b-4bd1-ad59-c995ac686cde"},{"id":"7edb2329-9dce-4007-a1fd-2b812eeae250","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-7b64090f","description":"Mission Summary: At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag , our ML-powered multimodal data mining framework, is the engine that powers this discovery.\n As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack. \n What You'll Do: \n \n Define Technical Strategy \u0026 Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs.\n Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters.\n Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment.\n Elevate Engineering Excellence : Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning.\n Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges.\n Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design. \n \n What We're Looking For (Must-Haves): \n \n BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)\n 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems\n Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)\n Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy\n Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)\n Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale\n Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency\n Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams \n \n Bonus Points (Nice-to-Haves): \n \n MS/PhD in Computer Science, Machine Learning, or a related field.\n Background in autonomous driving, robotics, or complex real-time decision-making systems.\n Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning.\n Familiarity with multimodal learning, sensor fusion, or large foundation models.\n Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms.\n Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms. \n \n We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and busine","salary_min":205000,"salary_max":272500,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","generative-ai","pytorch","autonomous-vehicles","tensorflow","mlops","robotics","gpu"],"apply_url":"https://motional.com/open-positions/?gh_jid=7730526003#/7730526003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-11T15:25:03Z","expires_at":"2026-06-29T14:06:01.141509Z","created_at":"2026-05-12T14:07:05.560434Z","updated_at":"2026-05-30T14:06:01.258746Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7edb2329-9dce-4007-a1fd-2b812eeae250"},{"id":"17520115-54f9-4e33-b1a5-732fa3c4b2b4","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-c89d556f","description":"Mission Summary: At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag , our ML-powered multimodal data mining framework, is the engine that powers this discovery.\n As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack. \n What You'll Do: \n \n Define Technical Strategy \u0026 Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs.\n Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters.\n Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment.\n Elevate Engineering Excellence : Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning.\n Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges.\n Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design. \n \n What We're Looking For (Must-Haves): \n \n BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)\n 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems\n Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)\n Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy\n Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)\n Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale\n Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency\n Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams \n \n Bonus Points (Nice-to-Haves): \n \n MS/PhD in Computer Science, Machine Learning, or a related field.\n Background in autonomous driving, robotics, or complex real-time decision-making systems.\n Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning.\n Familiarity with multimodal learning, sensor fusion, or large foundation models.\n Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms.\n Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms. \n \n We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and busine","salary_min":205000,"salary_max":272500,"location":"Las Vegas, Nevada, United States","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["robotics","pytorch","tensorflow","autonomous-vehicles","deep-learning","fine-tuning","generative-ai","mlops"],"apply_url":"https://motional.com/open-positions/?gh_jid=7730339003#/7730339003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-11T15:24:16Z","expires_at":"2026-06-29T14:06:01.060504Z","created_at":"2026-05-12T14:07:05.311464Z","updated_at":"2026-05-30T14:06:01.172978Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/17520115-54f9-4e33-b1a5-732fa3c4b2b4"},{"id":"6f58d53b-bb24-47d4-8fed-990f5442efab","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-613ad1ea","description":"Mission Summary: At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag , our ML-powered multimodal data mining framework, is the engine that powers this discovery.\n As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack. \n What You'll Do: \n \n Define Technical Strategy \u0026 Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs.\n Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters.\n Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment.\n Elevate Engineering Excellence : Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning.\n Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges.\n Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design. \n \n What We're Looking For (Must-Haves): \n \n BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)\n 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems\n Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)\n Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy\n Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)\n Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale\n Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency\n Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams \n \n Bonus Points (Nice-to-Haves): \n \n MS/PhD in Computer Science, Machine Learning, or a related field.\n Background in autonomous driving, robotics, or complex real-time decision-making systems.\n Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning.\n Familiarity with multimodal learning, sensor fusion, or large foundation models.\n Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms.\n Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms. \n \n We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and busine","salary_min":205000,"salary_max":272500,"location":"Pittsburgh, PA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["mlops","gpu","generative-ai","pytorch","fine-tuning","robotics","autonomous-vehicles","tensorflow"],"apply_url":"https://motional.com/open-positions/?gh_jid=7730335003#/7730335003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-11T15:24:14Z","expires_at":"2026-06-29T14:06:00.902359Z","created_at":"2026-05-12T14:07:05.480117Z","updated_at":"2026-05-30T14:06:01.015301Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6f58d53b-bb24-47d4-8fed-990f5442efab"},{"id":"85e7af4e-61c3-4faa-9a57-d53b03e1e9f8","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-b5c90575","description":"Mission Summary: At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag , our ML-powered multimodal data mining framework, is the engine that powers this discovery.\n As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack. \n What You'll Do: \n \n Define Technical Strategy \u0026 Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs.\n Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters.\n Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment.\n Elevate Engineering Excellence : Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning.\n Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges.\n Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design. \n \n What We're Looking For (Must-Haves): \n \n BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)\n 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems\n Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)\n Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy\n Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)\n Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale\n Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency\n Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams \n \n Bonus Points (Nice-to-Haves): \n \n MS/PhD in Computer Science, Machine Learning, or a related field.\n Background in autonomous driving, robotics, or complex real-time decision-making systems.\n Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning.\n Familiarity with multimodal learning, sensor fusion, or large foundation models.\n Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms.\n Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms. \n \n We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and busine","salary_min":205000,"salary_max":272500,"location":"Boston, MA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["tensorflow","mlops","autonomous-vehicles","pytorch","generative-ai","gpu","deep-learning","fine-tuning"],"apply_url":"https://motional.com/open-positions/?gh_jid=7730259003#/7730259003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-11T15:24:13Z","expires_at":"2026-06-29T14:06:00.982537Z","created_at":"2026-05-12T14:07:05.397761Z","updated_at":"2026-05-30T14:06:01.091927Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/85e7af4e-61c3-4faa-9a57-d53b03e1e9f8"},{"id":"d2ef167a-6317-434a-b0f6-d1e62d06f745","company_id":"5ce35db1-ec27-4df5-8ea1-167a8e9efac6","title":"Senior Software Engineer, Machine Learning (Ads)","slug":"senior-software-engineer-machine-learning-ads-94cf1a58","description":"Discord is used by over 200 million people every month for many different reasons, but there’s one thing that nearly everyone does on our platform: play video games. Over 90% of our users play games, spending a combined 1.5 billion hours playing thousands of unique titles on Discord each month. Discord plays a uniquely important role in the future of gaming. We are focused on making it easier and more fun for people to talk and hang out before, during, and after playing games.\n We are looking for a Senior Software Engineer specializing in Machine Learning to join our Revenue ML team at Discord. This team partners with our revenue product groups, focusing on both consumer revenue and our emerging Ads initiative. This role will specifically contribute to our Ads ML efforts, helping to build and scale ML capabilities in areas such as ads measurement, targeting, and delivery ranking.\n As part of this team, you will play a critical role in developing foundational ML models that enhance ad relevance, optimize performance, and drive revenue. This is a unique opportunity to work on an early-stage Ads ML platform and have a direct impact on the business's success. Our tech stack includes Python, ML frameworks like PyTorch and TensorFlow, large-scale data infrastructure, and real-time ad-serving technologies.\n What You'll Be Doing: \n \n Design, develop, and deploy machine learning models for ads targeting and ranking.\n Develop sophisticated ML solutions such as identity graph to enhance ad targeting.\n Build and optimize ad ranking models to serve the most effective ads based on campaign objectives (e.g., app installs, link click).\n Improve ads targeting and ranking by leveraging both on-platform and off-platform signals.\n Collaborate cross-functionally with product, engineering, and business teams to define and execute on the Ads ML roadmap.\n Scale our ML infrastructure to support an increasing number of concurrent ad campaigns while ensuring low-latency decision-making.\n Drive research and implementation of state-of-the-art ML techniques in the field of online advertising.\n \n What You Should Have: \n \n 5+ years of experience as a Machine Learning Engineer or Data Scientist.\n 3+ years of experience specifically in Ads ML (ads ranking, personalization, optimization, privacy-compliant user modeling, targeting, or measurement).\n Strong proficiency in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.\n Experience with applied deep learning (e.g transformers, embedding models).\n Proven track record of designing, implementing, and scaling ML-driven ad systems in real-world applications.\n Experience working with real-time ML inference, A/B testing, and optimization frameworks.\n Experience translating ML evaluation results and performance metrics into actionable product roadmap items.\n Ability to connect business objectives to ML solutions, with the flexibility to shift focus toward the highest-impact problems as priorities evolve.\n \n Bonus Skills: \n \n Strong understanding of performance advertising and how ML impacts revenue and advertiser retention.\n Knowledge of ad tech industry standards and ads ecosystem including targeting, retrieval, ranking, pacing, frequency, auction, etc.\n Experience with large-scale recommendation systems.\n Experience with large-scale data infrastructure and distributed computing\n \n The US base salary range for this full-time position is $220,000 to $247,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits. \n Why Discord?  Discord plays a uniquely important role in the future of gaming. We're a multiplatform, multigenerational and multiplayer platform that helps people deepen their friendships around games and shared interests. We believe games give us a way to have fun with our favorite people, whether listening to music together or grinding in competitive matches for diamond rank. Join us in our mission! Your future is just a click away! \n Discord is committed to inclusion and providing reasonable accommodations during the interview process. We want you to feel set up for success, so if you are in need of reasonable accommodations, please let your recruiter know.\n Please see our Applicant and Candidate Privacy Policy for details regarding Discord’s collection and usage of personal information relating to the application and recruitment process by clicking  HERE.","salary_min":220000,"salary_max":247000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","embeddings","tensorflow","pytorch","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/discord/jobs/8538039002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-07T21:10:51Z","expires_at":"2026-06-29T14:17:58.635403Z","created_at":"2026-05-08T14:19:22.32094Z","updated_at":"2026-05-30T14:17:58.745085Z","company_name":"Discord","company_slug":"discord","company_logo_url":"https://www.google.com/s2/favicons?domain=discord.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d2ef167a-6317-434a-b0f6-d1e62d06f745"},{"id":"fbf2725c-74be-4bef-ab5b-fbfb033ad841","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Senior Embedded Software Engineer","slug":"senior-embedded-software-engineer-b8d1a923","description":"Mission Summary: \n Motional’s onboard autonomous driving system team works at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s next-generation on-board autonomous driving system. As part of this team, the High Performance Compute Platform team is responsible for Motional’s current and next-generation onboard autonomous driving system. If you are a software engineer and love the idea of working on embedded AI hardware and software compute systems to create the next generation of autonomous vehicles, we would love to talk with you. We are hiring in a range of levels, from graduate engineers to Staff engineers. \n What You'll Be Doing: \n \n Design and develop infrastructure software on various hardware platforms for applications such as vision processing, radar systems, safety monitoring, etc., to be run on self-driving vehicles.\n Design test harnesses for embedded software components and full systems\n Provide technical mentorship to engineers.\n Proactively work with cross-functional engineering teams to solve complex and interesting problems. \n \n What We're Looking For: \n \n Experience with creating detailed requirements from use cases.\n Ability to lead a technical initiative, including breaking down work and guiding other engineers through execution.\n Experience writing software for embedded platforms in C and C++.\n Experience with Test-Driven Development (TDD).\n Experience working on embedded Linux / RTOSs.\n Experience working with networks (Ethernet, CAN etc.) and the common networking protocols.\n Experience with debugging on embedded platforms.\n Experience writing software in Python and experience doing automation with shell scripting.\n Experience working with ARM Cortex MCUs or Microprocessors. \n \n Bonus Points (not required): \n \n Experience working with large data pipelines, and platforms that require deterministic execution.\n Experience using inter-system communication protocols such as I2C and SPI\n Experience deploying Machine Learning models.\n Experience working with GPUs.\n Experience working directly with the Linux kernel or Device Drivers.\n Experience with Simulation and Code Generation, and knowing when their use is appropriate.\n Experience working with Bazel.\n Experience integrating various sensors, including Cameras, IMUs, Radars, LIDARs.\n Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks.  \n \n We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  \n Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. \n Salary Range\n $149,000 — $198,500 USD \n Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. \n Our journey is always people first. \n We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.\n Higher purpose, greater impact. \n We’re creating first-of-its-kind technology that will transform transportation. To do so successfully, we must design for everyone in our cities and on our roads. We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do.  \n Scale up, not starting up. \n Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges.\n Formed as a joint venture between","salary_min":149000,"salary_max":198500,"location":"Las Vegas, Nevada, United States","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","pytorch","tensorflow","code-generation","data-pipeline","embedded","infrastructure"],"apply_url":"https://motional.com/open-positions/?gh_jid=7727296003#/7727296003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-07T15:18:01Z","expires_at":"2026-06-29T14:05:59.353607Z","created_at":"2026-05-08T14:06:09.173742Z","updated_at":"2026-05-30T14:05:59.465389Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fbf2725c-74be-4bef-ab5b-fbfb033ad841"}],"page":1,"per_page":20,"total":470,"total_pages":24}