Staff Machine Learning Engineer, AI Infrastructure
About The Position
Waymo’s Driver Understanding and Evaluation (DUE) team develops metrics and technologies to understand the Waymo Driver’s real-world behavior, analyze driving contexts and scenes, augment real-world data to generate rare events, build data infrastructure, and improve agents and simulation. The DUE Machine Learning team builds and operates scalable machine learning and data systems, simulation workflows, and insight tools to improve evaluation and developer workflows. It combines human judgments and machine learning models to produce training and evaluation data for the metrics and components that make up the Waymo Driver. The Staff Machine Learning Engineer will lead the design and development of machine learning infrastructure spanning data, training, and production serving. The role will scale distributed systems for dataset generation, training, and evaluation; ensure production serving is reliable and efficient; maintain consistency between offline evaluation and production inference; and provide technical direction and mentorship. The engineer will work with modeling, evaluation, and platform teams to translate product and business goals into measurable technical deliverables.
Requirements
- Bachelor’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
- At least 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 user-facing APIs and framework internals.
- Proven ability to set technical direction and influence partner teams in ambiguous problem spaces.
Nice To Haves
- Master’s or Ph.D. in Computer Science or a related field, specializing in machine learning.
- At least 10 years of hands-on experience building production machine learning systems.
- Experience serving ML models in production at scale, including low-latency inference on TPUs or GPUs, SLOs, and safe model rollouts.
- Technical leadership serving production-quality models, including ownership of launches, reliability, and on-call for services used by other teams.
- Experience with distributed training and accelerator performance profiling.
- Experience in autonomous vehicles, robotics, or large-scale simulation.
Responsibilities
- Lead the design and development of ML infrastructure that takes DUE models from data through 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 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 evaluated models are the models served.
- Drive technical direction, guide architectural decisions, and mentor engineers.
- Collaborate with modeling, evaluation, and platform teams to turn product and business goals into measurable technical deliverables.
Benefits
- Discretionary annual bonus program
- Equity incentive plan
- Company benefits program
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What This Job Offers
Job Type
Full-time
Career Level
Senior
