Sr Software Engineer - Av Performance - Av Labs

Uber Uber · Consumer · Sunnyvale, CA · Engineer - Software Engineering - Backend

Seeking a Sr. Software Engineer to join Uber's AV Labs in Sunnyvale, CA. This role focuses on developing and deploying high-performance software systems for autonomous vehicle performance, with a strong emphasis on optimizing ML models and inference pipelines for real-time embedded hardware. The engineer will collaborate with ML researchers and engineers to integrate advanced models into production.

What you'd actually do

  1. Develop and deploy high-performance, scalable, and reliable software systems for autonomous vehicle (AV) performance.
  2. Optimize ML models and inference pipelines for real-time performance on embedded hardware.
  3. Collaborate with ML researchers and engineers to integrate cutting-edge models into production systems.
  4. Design and implement robust testing and validation frameworks to ensure the safety and reliability of AV software.
  5. Contribute to the architectural design and technical roadmap for AV performance systems.

Skills

Required

  • 5+ years of experience in software engineering, with a focus on performance-critical systems.
  • Strong proficiency in C++ and Python.
  • Experience with optimizing ML models for inference on embedded hardware (e.g., GPUs, TPUs).
  • Familiarity with ML frameworks such as TensorFlow, PyTorch, or JAX.
  • Experience with distributed systems and cloud platforms (e.g., AWS, GCP).
  • Excellent problem-solving and debugging skills.

Nice to have

  • Experience with autonomous vehicle systems or robotics.
  • Knowledge of real-time operating systems (RTOS).
  • Familiarity with containerization technologies (e.g., Docker, Kubernetes).
  • Experience with performance profiling and analysis tools.

Other signals

  • Develop and deploy high-performance, scalable, and reliable software systems for autonomous vehicle (AV) performance.
  • Optimize ML models and inference pipelines for real-time performance on embedded hardware.
  • Collaborate with ML researchers and engineers to integrate cutting-edge models into production systems.