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Staff Machine Learning Engineer, Maneuvering Tech at Waymo (Mountain View, CA / San Francisco, CA / Pittsburgh PA, United States)

Waymo· Mountain View, CA / San Francisco, CA / Pittsburgh PA, United States· $251k–$310k·

Role details

Salary
$251k–$310k
Level
Staff

Job description

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.

Driver Refinement and Validation’s (DRV) mission is to build upon the high-quality ML Driver developed by Driver Intelligence, refining its performance to improve autonomous driving in specific, complex real-world contexts and problem areas (e.g., Ride-Hailing contexts). It aims to ensure that the Waymo Driver always computes safe, compliant, comfortable, and useful plans. As an Engineer in DRV, you will bridge the gap between cutting-edge machine learning and real-world vehicle performance, engineering the validation and onboard systems that ensure the autonomous vehicle reliably executes intelligent and predictable maneuvers.

You will:

  • Develop next-generation, ML-powered systems that enhance the capabilities of the ML driver and accelerate the rapid scaling of Waymo’s business
  • Fine-tune, adapt, and benchmark state-of-the-art generative models to improve autonomous vehicle perception, motion prediction, and trajectory planning
  • Write high-quality, scalable, and thoroughly tested code to bring cutting-edge ML into production
  • Partner with world-class engineers and product managers to deliver safe and smooth autonomous vehicle behaviors

You have:

  • MS in Computer Science, Machine Learning, Robotics, or related technical field, or equivalent practical experience
  • 8+ years of industry experience deploying, evaluating, and maintaining ML-based systems in real-world, production environments
  • Proficient programming skills in Python and/or C++, coupled with strong analytical and debugging abilities
  • Expertise with modern deep learning frameworks, such as  JAX or PyTorch

We prefer:

  • Experience in deep learning, reinforcement learning, and ML systems
  • Domain expertise in solving prediction, behaviors, motion planning, or related robotics problems
  • ML post-training experience
  • Prior industry experience (e.g. internships) in applied ML or software development
  • Publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA,  IROS, RSS, CoRL, ACL, or EMNLP)

The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.

Hourly PhD Pay $251,000—$310,000 USD

Why work at Waymo

  • Impact: Work on technology that directly saves lives (1.4M traffic deaths globally each year) and reshapes the future of transportation.
  • Culture: Waymo describes itself as a place for “ambitious thinkers and relentless problem solvers” with a deep respect for getting every detail right. Values include safety, innovation, and responsibility.
  • Teams: Opportunities across Software Engineering, Hardware Engineering, AI Foundations, Product & Design, Operations & Supply Chain, Safety, Policy, and General & Administrative roles.
  • Internships: Strong early-career program with internships for Bachelors, Masters, MBA, and PhD candidates, often leading to full-time offers.
  • Hiring process: Transparent and structured. Includes an initial recruiter screen, 1-2 phone/video interviews, and a virtual onsite (up to 5 interviews). Emphasizes problem-solving, collaboration, and real-time thinking. AI tools are not permitted during technical assessments to ensure fair evaluation.
  • Remote/hybrid policy: The careers page lists both on-site and remote roles, with most engineering and operations roles based in Mountain View, San Francisco, or other US locations.
  • Benefits: Top-notch benefits, unique learning opportunities, thoughtful community engagement, and meaningful gatherings. Specific perks are not detailed publicly but are described as comprehensive.

Application questions