Staff ML Engineer - Driver Evaluation & Simulation

Waymo; Mountain View (CA); On-site; USD 251,000 - 310,000; Full time

Benefits offered by this job

Discretionary annual bonus

Equity incentive plan

Generous benefits program

Job summary

Waymo is seeking researchers and software engineers to advance machine learning evaluation systems for the Waymo Driver. You will build scalable ML infrastructure, data systems, and simulation tools to support hundreds of metrics and components of autonomous driving technology.

The role emphasizes leading large-scale model architectures, training, deployment, and collaboration across teams to improve system performance and reliability.

Qualifications

• M.S. or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or related field, or equivalent practical experience.
• 7+ years of professional software engineering experience, with at least 3 years in ML infrastructure.
• Experience contributing to ML tooling/frameworks (e.g., PyTorch, Jax, TensorFlow, Ray).
• Strong expertise in distributed training techniques and scaling ML models.
• Understanding autoregressive transformers and state-of-the-art ML models.
• Demonstrated leadership across cross-functional teams and technical directions.

Responsibilities

• Provide deep technical leadership on large-scale ML model architectures for autonomous vehicle models.
• Build scalable systems for training and fine-tuning large-scale models to evaluate driving behaviors.
• Work at the intersection of data engineering, model development, and simulation.
• Oversee production and optimization of ML models for Waymo’s fleet traveling millions of miles.
• Design and scale distributed systems covering the ML lifecycle and planet-scale data generation.
• Collaborate cross-functionally to translate product goals into measurable technical deliverables.

Job description

Waymo is seeking researchers and software engineers to advance machine learning evaluation systems for the Waymo Driver. You will build scalable ML infrastructure, data systems, and simulation tools to support hundreds of metrics and components of autonomous driving technology.

The role emphasizes leading large-scale model architectures, training, deployment, and collaboration across teams to improve system performance and reliability.