{"id":2571442,"url":"https://alion.io/job/uber-machine-learning-engineer-ii-semantics-av-labs","title":"Machine Learning Engineer II - Semantics, AV Labs","company":{"id":230,"name":"Uber","domain":"uber.com","url":"https://alion.io/company/uber","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":93,"open_postings":123,"ghost_share":0,"stale_share":0.455,"repost_share":0.057,"time_to_fill_p50_days":8,"computed_at":"2026-10-15T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":171000,"max":190000,"currency":"USD","period":"year","gross":null,"usd_annual":190000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"C++","optional":true},{"name":"Computer Vision","optional":true}],"status":"live","first_seen_at":"2026-08-05T00:17:53Z","employer_posted_date":"2026-08-05","last_verified_at":"2026-10-16T01:40:14Z","board_verified":true,"closed_at":null,"days_open":72,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":72},"description":"About the Role\nUber is launching AV Labs to accelerate the autonomous technology ecosystem. We're building out a high-velocity team of multi-disciplinary experts to turn real-world operations into high-quality data for our autonomous partners. This team is focused on the hardest problem in AV today: unlocking real-world, long-tail driving data. Autonomy is now a data race-and Uber has an edge: We collect rare, real-world driving data at a scale and capital efficiency no one else can match.\nAs a ML Engineer, you will be at the forefront of Physical AI, building advanced autonomy algorithms and models to add rich semantics to our massive driving data. You will be responsible for the development and implementation of the latest machine learning techniques that enables better data mining, deep scene understanding, and causal modeling of ego vehicle behavior.The ideal candidate will be able to identify complex edge cases, provide robust algorithmic solutions, and set a high technical excellence bar.\nWhat You Will Do\nAlgorithm Development: Developalgorithms and foundation models that extract high-fidelity semantic meaning from complex urban edge cases to enrich our L4 data lake.\nSystem Design: Implement scalable ML systems, including management of upstream sensor dependencies.\nDataset Optimization: Deliver high-quality datasets to accelerate ML technologies through advanced sensor data collection, processing, and auto-labeling.\nCross-Functional Collaboration: Partner with platform, product, and security engineering teams to enable the successful deployment of the latest machine learning techniques into production.\nBasic Qualifications\n2+ years of working experience in the ML/Robotics industry.\nBachelor’s degree (or higher) in Computer Science, Computer Engineering, or related fields.\nProficient in Python and Linux environments.\nFamiliar with modern AI/ML frameworks (e.g., PyTorch).\nPreferred Qualifications\nExperience in the Autonomous Driving domain.\nProven track record of deploying ML models in safety-critical physical systems.\nMaster’s or PhD degree in Computer Vision, Robotics, or Machine Learning.\nFamiliarity with C++ and high-performance computing.\n For Sunnyvale, CA-based roles: The base salary range for this role is USD $171,000 per year - USD $190,000 per year.\nYou will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. 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