Machine Learning Engineer - Semantics - AV Labs

Job-ID: 157796Category: EngineerLocation: Sunnyvale, CA, United StatesSchedule: Full time, Level 5Posted: 2026-06-19About the RoleUber 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.As a Senior 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.What the Candidate Will DoAlgorithm Development: Lead the development of autonomy algorithms and foundation models that extract high-fidelity semantic meaning from complex urban edge cases to enrich our L4 data lake.Systems Architecture Design: Architect scalable ML systems, including management of upstream sensor dependencies.Technical Leadership: Partner with fellow engineers to architect, design, and build scalable solutions for ML technology that can stand the test of scale and availability.Dataset Optimization: Deliver high-quality datasets to accelerate ML technologies through advanced sensor data collection, processing, and auto-labeling.Cross-Functional Collaboration: Partner with platform, product, and security engineering teams to enable the successful deployment of the latest machine learning techniques into production.Basic Qualifications4+ years of working experience in the ML/Robotics industry.Bachelor’s degree in Computer Science, Computer Engineering, or related fields.Proficient in Python and Linux environments.Familiar with modern AI/ML frameworks (e.g., PyTorch).Preferred QualificationsExperience in the Autonomous Driving domain.Proven track record of deploying ML models in safety-critical physical systems.Master’s or PhD degree in Computer Vision, Robotics, or Machine Learning.Familiarity with C++ and high-performance computing. ~~ ~~ResponsibilitiesFor Sunnyvale, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.About the companyReady to Ride?This isn't the kind of place where you follow a playbook — it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves — we'd love to hear from you.You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.