Senior Staff ML Engineer, (TLM) Driver Understanding and Evaluation

<div class="content-intro"><p>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.</p></div><p>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 looking for researchers and software engineers who are passionate about developing machine learning techniques. These techniques are for the Evaluation systems on our autonomous service. They will serve as a constant driver to improve the performance of our technology stack.</p> <p> </p> <p><strong>You will:</strong></p> <ul> <li>Grow the end-to-end strategy for our next generation of machine learning-based evaluation metrics, promoting scientific and statistical rigor across our embodied AI applications.</li> <li>Architect and build scalable systems for training and fine-tuning large-scale generative models to produce realistic and evaluate interesting driving behaviors.</li> <li>Lead the design, implementation, and iteration of novel RL algorithms, reward functions, and training paradigms tailored for generating high-fidelity and insightful driving behaviors.</li> <li>Lead the development of cutting-edge Deep Learning models and Generative AI (LLM/VLM) solutions. These solutions will enhance human-led triaging, introduce automation for high-volume workflows, and perform nuanced analysis of self-driving behavior to detect critical anomalies.</li> <li>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.</li> <li>Provide technical mentorship, guidance, and thought leadership to other engineers within the team and across collaborating groups.</li> <li>Guide and align multiple teams—including Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—on a cohesive evaluation strategy, ensuring cross-functional alignment on goals and priorities.</li> </ul> <p> </p> <p><strong>You have:</strong></p> <ul> <li>PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.</li> <li>7+ years of hands-on experience in developing and applying Machine Learning models, with a significant focus on Reinforcement Learning.</li> <li>Demonstrated expertise in deep learning, sequence modeling, and generative models.</li> <li>Strong publication record or history of impactful project delivery in RL or related areas.</li> <li>Proficiency in Python and standard ML frameworks (e.g., JAX, TensorFlow).</li> <li>Experience with large-scale distributed training and data processing.</li> <li>Proven ability to lead complex and ambiguous technical projects from conception to completion.</li> </ul> <p> </p> <p><strong>We prefer:</strong></p> <ul> <li>10+ years of relevant experience in ML/RL research and application.</li> <li>Experience in the autonomous vehicles domain, robotics, or complex simulation environments.</li> <li>Deep understanding of state-of-the-art RL techniques, inc ... (truncated, view full listing at source)