Drimva

Waymo

Staff Machine Learning Engineer, Maneuvering Tech

$251K–$310K

Mountain View, CA, USA +5 · Staff · Full-time · Posted Jun 11, 2026

Location
Mountain View, CA, USA
San Francisco, CA, USA
Pittsburgh, PA, USA
Mountain View, California, United States
Pittsburgh, Pennsylvania, United States
San Francisco, California, United States
Experience
8+ years of industry experience.
Education
MS in Computer Science, Machine Learning, Robotics, or a related technical field, or equivalent practical experience.

About the job

Develops and validates machine-learning systems that improve autonomous-vehicle perception, prediction, and planning and bring reliable maneuvers into production.

What you'll do

  • Develop ML-powered systems to enhance the ML driver and support business scaling.
  • Fine-tune, adapt, and benchmark generative models for perception, motion prediction, and trajectory planning.
  • Write tested, scalable production code and collaborate to deliver safe, smooth vehicle behaviors.

What they ask for

  • MS in Computer Science, Machine Learning, Robotics, or a related technical field, or equivalent practical experience.
  • 8+ years of industry experience deploying, evaluating, and maintaining production ML systems.
  • Proficiency in Python and/or C++, with strong analytical and debugging abilities.
  • Expertise with modern deep learning frameworks such as JAX or PyTorch.

Nice to have

  • Experience in deep learning, reinforcement learning, and ML systems; domain expertise in prediction, behaviors, motion planning, or related robotics problems.
  • ML post-training experience; applied ML or software development industry experience; or publications in listed top-tier conferences.

Pay

$251,000 — $310,000 USD (base salary; listed as hourly PhD pay)

Tools

Python, C++, JAX, PyTorch

AI focus

Fine-tune and benchmark generative models and develop ML systems for autonomous-vehicle perception, motion prediction, trajectory planning, and maneuvering.

Model-written summary · the original posting is authoritative

First seen Oct 10, 2026 · Last verified Nov 5, 2026 (UTC)