Senior Machine Learning Engineer - AV Labs

About the Role

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 will be 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 (millions of Uber trips every hour across cities, conditions, and edge cases create the data autonomy has been missing). We will build platforms that harness scale and real-world complexity to reimagine how the world moves.

You will be an AI/ML engineer in AV Labs and involved in the development and implementation of the latest machine learning techniques for computer vision and perception use cases. The ideal candidate will be able to identify issues, provide solutions and implement the fixes as well as setting a high technical excellence bar in all things we do. You’ll be able to collaborate with other engineers across networking, storage, compute, big data and cloud engineering, as well as with partner engineering teams which enables Uber’s mission of helping people go anywhere and get anything and earn their way.

What You Will Do

• Design and deliver software and tools as part of our state-of-the-art Machine Learning platform.
• Systems architecture design, including management of upstream and downstream dependencies.
• Provide technical leadership, influence and partner with fellow engineers to architect, design and build scalable solutions for ML technology that can stand the test of scale and availability, while reducing operational overhead.
• Deliver datasets to accelerate ML technologies, sensor data collection, processing, labeling, indexing, etc
• Participate in periodic on-call rotations and be available for critical issues.
• Collaborate with platform, product and security engineering teams, and enable successful use of the latest machine learning techniques.

Basic Qualifications

• Minimum 4 years of working experience in the ML/Robotics industry
• Bachelor degree in computer science, computer engineering or related fields
• Proficient in Python and Linux
• Familiar with OpenCV, TensorFlow/PyTorch

Preferred Qualifications

• Master or PhD degree in computer vision or robotics
• Familiar with C++
• Familiar with the Robot Operating System (ROS)

For San Francisco, 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 .

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.