Uber 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 PhD Software Engineer II, 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 you’ll do
- Algorithm Development: Develop algorithms and foundation models that extract high-fidelity semantic meaning from complex urban edge cases to enrich our L4 data lake
- System Design: Implement scalable ML systems, including management of upstream sensor dependencies
- 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 Qualifications
- Completing or recently completed a PhD in Computer Science, Robotics, Machine Learning, Computer Vision, Electrical Engineering, or a related technical field
Preferred Qualifications
- Strong publication record in top-tier AI, ML, robotics, or computer vision conferences
- Deep knowledge of machine learning for robotics, computer vision, or autonomous systems
- Experience working with large-scale sensor data (e.g., camera, LiDAR) and building data pipelines for ML applications
- Strong proficiency in Python and experience with modern ML frameworks such as PyTorch
- Experience developing or deploying ML models in real-world or safety-critical systems
- Familiarity with C++ and high-performance or real-time systems
- Proven ability to translate research into production-grade systems
- Excellent communication skills, with the ability to explain complex technical concepts to cross-functional stakeholders
For San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year.
For all US locations, 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. More details can be found at the following link https://jobs.uber.com/en/benefits.
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.
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.
Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.