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 Simulator Team at Waymo builds state-of-the-art simulations of realistic environments for evaluating, training and scaling the Waymo Driver. Your team will be a diverse, and collaborative group of machine learning (ML) engineers, software engineers and data scientists. We develop industry-leading simulation solutions using advanced ML algorithms that measure and enhance the performance of the Waymo Driver. We achieve those goals by jointly modeling the real world, including realistic agents (vehicles, pedestrians, cyclists, motorcyclists), static road elements, traffic control systems, weather conditions, and the full sensor suite including camera, lidar and radars.
We enhance our simulations' fidelity, scalability, and controllability by leveraging state of the art machine learning. This includes large language models, foundational world models, and reconstructive methods, all trained on datasets using both generative and reconstructive AI, alongside traditional rendering techniques.
In this hybrid role, you will report to the Director of Engineering, Simulator.
You will:
- Serve as the connective tissue for the London office, ensuring that engineers across Sim Realism, DUE, AIF, and other functions feel part of a single, cohesive "One Waymo" site.
- Act as the primary executive conduit between Waymo's Foundation Model initiatives and Google DeepMind teams in the UK to accelerate joint R&D.
- Define the technical roadmap for the London Sim Realism team, driving tight alignment with Waymo's overarching simulation and AI-first objectives.
- Direct the research and engineering of cutting-edge generative models for sensor-level and semantic-level simulation (Waymo World Model).
- Recruit, manage, and mentor a top-tier Sim Realism engineering organization, attracting elite talent in machine learning, generative AI, and simulation.
- Partner closely with Real Estate & Workplace Services (REWS) to design and support a site strategy.
You have:
- A PhD or Master's degree in Computer Science, Machine Learning, Robotics, or a related field (or equivalent practical experience).
- A proven track record of leading, scaling, and managing large engineering organizations in a fast-paced, research-driven, and highly technical environment.
- Deep foundational knowledge and applied expertise in machine learning, generative foundation models, and/or complex simulation environments.
- Demonstrated ability to drive complex, multi-year, cross-functional projects from early conception to global impact.
- Extensive experience managing and developing senior engineers, staff engineers, and technical leads/managers.
We prefer:
- Familiarity with autonomous vehicle technology, robotics, or complex embodied AI systems.
- Exceptional executive communication skills, with a proven ability to build consensus and influence stakeholders across reporting lines.
- Demonstrated capability to translate highly ambiguous, long-term strategic goals into focused, actionable engineering roadmaps.
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.
