About The Position

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. This role is at the intersection of robotics and machine learning, driving the next generation of operational efficiency for Waymo's rapidly expanding autonomous fleet. You will lead efforts to generalize complex depot operations—such as exterior cleaning, sensor calibration, and maintenance checks—using advanced robotics. Key work involves leveraging foundation models, reinforcement learning, simulation, and integrating ML models in production at scale. You will interface closely with operations teams to translate real-world needs into robust, working solutions. This role follows a hybrid work schedule and reports to a Director, Hardware and Sensors.

Requirements

  • At least 10 years of experience applying machine learning techniques to large-scale industrial problems.
  • Proven experience in training and evaluating large machine learning models.
  • Expertise in reinforcement learning and its applications to real-world problems.

Nice To Haves

  • PhD in Machine Learning, Robotics, or a related technical field.
  • Experience in robotics or embodied AI is a plus.
  • Background in collaborating with internal and external research partners on applying ML to large-scale industry scale problems.

Responsibilities

  • Drive the next generation of operational efficiency for Waymo's rapidly expanding autonomous fleet.
  • Lead efforts to generalize complex depot operations using advanced robotics.
  • Focus on complex depot operations, such as exterior cleaning, sensor calibration, and maintenance checks.
  • Leverage foundation models, reinforcement learning, and simulation.
  • Integrate ML models in production at scale.
  • Interface closely with operations teams to translate real-world needs into robust, working solutions.
© 2024 Teal Labs, Inc
Privacy PolicyTerms of Service