Staff Machine Learning Engineer, AI Infrastructure

The Driver Understanding and Evaluation (DUE) team at Waymo is developing rich metrics for understanding the behavior of the Waymo Driver in the real world, and technologies such as  context and scene analysis to understand driving, understanding and augmenting real world driving data to generate rare driving events, build large scale data infrastructure, improve components such as agents and a realistic simulator. These technologies come together to drive the overall technical strategy and methodology used to evaluate the behavior of the Waymo Driver. 

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. 

• Lead the design and development of ML infrastructure that takes DUE models from data to training to production serving, including event detection models, triage foundation models, and agents.
• Design and scale distributed systems for large-scale dataset generation, model training, and evaluation across Waymo's real-world and simulation data.
• Own the reliability, efficiency, and observability of production ML serving, including accelerator capacity planning and performance optimization.
• Keep offline evaluation and production inference consistent, so the models we evaluate are the models we serve.
• Drive technical direction for the team, guide architectural decisions, and mentor engineers.
• Collaborate with modeling, evaluation, and platform teams across Waymo to turn product and business goals into measurable technical deliverables.

• B.S. in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
• 7+ years of hands-on experience building machine learning systems.
• Experience designing, scaling, training, deploying, and optimizing large-scale ML systems from data to model.
• Strong coding experience in Python and/or C++.
• Experience with ML frameworks such as JAX, TensorFlow, or PyTorch, including both the user-facing APIs and their internals.
• Proven ability to set technical direction and influence partner teams in ambiguous problem spaces.

• M.S. or Ph.D. in Computer Science or a related field with a specialization in machine learning.
• 10+ years of hands-on experience building production machine learning systems.
• Experience serving ML models in production at scale, including low-latency inference on accelerators (TPU/GPU), SLOs, and safe model rollouts.
• Technical leadership in serving production-quality models, including owning launches, reliability, and on-call for services that other teams depend on.
• Experience with distributed training and accelerator performance profiling.
• Experience in autonomous vehicles, robotics, or large-scale simulation.