Staff Machine Learning Engineer

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. Lead the implementation, and iteration of novel RL algorithms, reward functions, and training paradigms tailored for generating high-fidelity and insightful driving behaviors. Lead the development of cutting-edge deep learning models and generative AI (LLM/VLM) solutions to enhance human-led triaging, introduce automation for high-volume workflows, and perform nuanced analysis of self-driving behavior to detect critical anomalies. Oversee the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions

of miles. Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a novel reinforcement learning from human preference (RLHF) based data collection and evaluation system. Collaborate closely with multiple teams (e.g., Prediction, Planning, Research), other technical leads, and senior leaderships across Waymo to deliver on key strategic efforts. You have M.S. or Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field; or equivalent practical experience. 7+ years of hands-on experience in developing and applying Machine Learning models, with a significant focus on reinforcement learning. Demonstrated expertise in deep learning, sequence modeling, and generative models. Strong publication record or history of impactful project delivery in RL or related areas. Proficiency in Python and standard ML frameworks (e.g., JAX, TensorFlow). Experience with large-scale distributed training and data processing. Proven ability to lead complex and ambiguous technical projects from conception to completion. We prefer 10+ years of relevant experience in ML/RL research and application. Experience in the autonomous vehicles domain, robotics, or complex simulation environments. Deep understanding of state-of-the-art RL techniques, including those used for fine-tuning large models (e.g., from human feedback/preferences). Familiarity with large-scale simulation platforms and their integration with ML training workflows. Experience designing and using metrics for evaluating complex AI systems. Track record of technical leadership, influencing senior stakeholders, and driving innovation across team boundaries. Excellent communication skills, with the ability to articulate complex technical concepts clearly. Salary Range 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. £155,000 - £163,000 GBP #J-18808-Ljbffr

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 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 judgement with advanced machine learning models, we deliver training and evaluation