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Staff Machine Learning Engineer (TLM), Driver Understanding and Evaluation at Waymo (London, United Kingdom)

Waymo· London, United Kingdom· £155k–£163k·

Role details

Salary
£155k–£163k

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.

The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver.

We are planning to set up a team in London UK to work with the teams in MTV and Oxford to build these foundation models out and to integrate them into several evaluation and training products. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack.

In this hybrid role, you will report to an Engineering Manager.

You will:

  • Be part of a world class research engineering team to grow the state-of-the-art of ultra realistic AV simulations using foundation models
  • Collaborate with teams in Waymo Oxford to use large models to improve sim realism
  • Design experiments that push the frontiers of AV simulations
  • Develop metrics that measure the realism of simulated worlds
  • Train and evaluate large models and integrate them into the simulator and its downstream applications
  • Help hire outstanding research engineers from diverse backgrounds
  • Be a part of a collaborative research engineering team that takes research ideas and productionizes them
  • 1+ years of people management experience

We prefer:

  • 7+ years experience in applied Deep Learning
  • 7+ years coding and design skills
  • Experience solving complex production problems using state-of-the-art ML techniques
  • Experience taking research to production
  • Expertise in Data Analysis or Data Science

The expected base salary range for this full-time position is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level.  Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.

Salary Range £155,000—£163,000 GBP

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