Machine Learning Engineer II - Semantics, AV Labs

Uber

Sunnyvale (CA)

On-site

USD 171,000 - 190,000

Full time

14 days+
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Benefits offered by this job

Bonus program
Equity award
401(k) plan
Benefits

Job summary

Uber is launching AV Labs to accelerate the autonomous technology ecosystem. The ML Engineer role focuses on Physical AI, building advanced autonomy algorithms to add semantic understanding to our driving data and tackle real-world edge cases.

You will develop scalable ML systems, optimize datasets through sensor data collection and auto-labeling, and collaborate with platform, product, and security teams to deploy models into production.

Qualifications

  • 2+ 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 such as PyTorch.

Responsibilities

  • Develop algorithms and models to extract high-fidelity semantic meaning from urban edge cases.
  • Implement scalable ML systems and manage upstream sensor dependencies.
  • Deliver high-quality datasets through advanced sensor data collection, processing, and auto-labeling.
  • Collaborate with platform, product, and security engineering teams to deploy ML techniques into production.

Skills

Python
Linux
PyTorch
ML systems

Education

Bachelor’s degree in CS/CE or related
Master’s or PhD in CV/Robotics/ML

Tools

C++
GCC
CUDA

Job description

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 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 ML Engineer, you will be at the forefront ofPhysical 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 Will 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
  • 2+ years of working experience in the ML/Robotics industry.
  • Bachelor’s degree (or higher) 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 $171,000 per year - USD $190,000 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.

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