Staff Machine Learning Engineer (Infra), Driver Understanding and Evaluation

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 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 for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack.

You will:

• Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models.


• Build scalable systems for training and fine-tuning large-scale models to evaluate interesting driving behaviors.


• Work at the intersection of data engineering, model development, and simulation Provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives.


• Oversee the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions of miles.


• Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation.


• Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.

You have:

• M.S. or Ph.D. degree Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.


• 7+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.


• A history of contributions to machine learning tooling and frameworks e.g. PyTorch, Jax, Tensorflow, Ray, or similar. The candidate should understand both the user facing API and the internal workings. 


• Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.


• Deep understanding of state-of-the-art machine learning models such as autoregressive transformers.


• Strong leadership skills with experience navigating cross-functional teams and providing technical leadership projects across multiple organizations.

We prefer:

• 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.


• 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.