Remote Autonomous Driving Engineer Jobs in California

About 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 Do- Algorithm 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 Qualifications- 4+ 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 Qualifications- Experience 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.For 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. More details can be found at the following link [https://jobs.uber.com/en/benefits](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](https://forms.gle/aDWTk9k6xtMU25Y5A).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.