About the job
Waymo is at the forefront of autonomous driving technology, dedicated to becoming the most trusted driver in the world. Originating from the Google Self-Driving Car Project in 2009, our mission has been to enhance mobility access while significantly reducing traffic-related fatalities. The Waymo Driver, our cutting-edge autonomous technology, powers a fully autonomous ride-hail service and is adaptable across various vehicle platforms and applications. With over ten million rider-only trips completed and more than 100 million miles driven autonomously on public roads, our system has been tested extensively, further driving our commitment to safety and innovation.
The Driver Understanding and Evaluation (DUE) Machine Learning team is tasked with developing and managing scalable machine learning and data systems, enhancing our simulation workflows and analytical tools, and refining the evaluation processes for developer onboarding. This team merges expert human insights with advanced machine learning techniques to provide robust training and evaluation data for the myriad metrics and components that define the Waymo Driver. We seek talented researchers and software engineers who are enthusiastic about pioneering machine learning approaches for our evaluation systems, driving continuous improvements in our technology stack.
Key Responsibilities:
- Develop a comprehensive strategy for next-generation, machine learning-based evaluation metrics while ensuring scientific and statistical rigor across our AI applications.
- Design and implement scalable systems for training and fine-tuning large-scale generative models to simulate realistic driving behaviors.
- Lead efforts in creating, implementing, and iterating innovative reinforcement learning (RL) algorithms and reward functions tailored to generate high-fidelity driving behaviors.
- Oversee the development of advanced Deep Learning models and Generative AI solutions to enhance triaging processes, automate high-volume workflows, and perform nuanced analyses of self-driving behaviors to identify critical anomalies.
- Stay informed on best practices within Alphabet and the broader industry to develop a state-of-the-art Reinforcement Learning from Human Preference (RLHF) based data collection and evaluation system.
- Provide mentorship, guidance, and thought leadership to engineers within the team and across collaborative groups.
- Coordinate and align multiple teams—including Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—to ensure cohesive evaluation methodologies.

