Staff Machine Learning Engineer
Waymo · London, United Kingdom · Simulation (7XW)
About this role
Waymo is hiring a staff-level Machine Learning Engineer based in London, United Kingdom. The posting calls out experience with Python, TensorFlow, LLMs, Deep Learning and roughly 7+ years of relevant work. Listed education preference: a master's degree or equivalent. Compensation is listed at £150,000–£162,000 per year.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- staff
- Track
- Tech leadership
- Employment
- Full-time
- Location
- London, United Kingdom
- Experience
- 7+ years
- Education
- Master's degree
- Department
- Simulation (7XW)
More roles at Waymo
Job description
from Waymo careersWaymo 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 ML Core London team builds and operates scalable machine learning systems, simulation workflows, and insight tools designed to improve the evaluation and developer onboarding journeys. By combining expert human judgment with advanced machine learning models, we deliver training and evaluation data for hundreds of metrics and components that comprise the Waymo Driver.
We are looking for researchers and software engineers passionate about developing ML techniques for evaluation systems and driving performance improvements across our technology stack.
You will:
- Build scalable systems for training and fine-tuning large-scale generative models to produce realistic and evaluate interesting driving behaviors.