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# Staff Machine Learning Engineer, Simulation Waymo London, UK

- **Company:** Waymo
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £155,000.0 - £163,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Machine Learning
- **Published:** August 14, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/x/43734043/

## About the Role

applicationsHelp hire outstanding research engineers from diverse backgroundsBe a part of a collaborative research engineering team that takes research ideas and productionizes themWe prefer:5+ years experience in applied Deep Learning5+ years coding and design skillsExperience solving complex production problems using state-of-the-art ML techniquesExperience taking research to productionExpertise in Data Analysis or Data Science#LI-HybridThe 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 #J-18808-Ljbffr

## Description

weather. To increase the fidelity and steerability of the simulations, we employ large foundation models, trained on our massive datasets that allow us to quickly setup and rollout multiple scenarios to subject our driver to.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 research engineers to work on these exciting problems.In this hybrid role, you will report to an Engineering Director.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 modelsCollaborate with teams in Waymo Oxford to use large models to improve sim realismDesign experiments that push the frontiers of AV simulationsDevelop metrics that measure the realism of simulated worldsTrain and evaluate large models and integrate them into the simulator and its downstream


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