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

About the role

The DUE Machine Learning team at Waymo is seeking researchers and software engineers to join our team. Our goal is to develop and operate scalable machine learning and data systems, simulation workflow and insight tools, and improve and speed up the evaluation and onboard developer journeys. We are looking for individuals who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles and have a relentless drive to improve the performance of our technology stack.

Responsibilities

• Build and operate scalable machine learning and data systems, simulation workflow and insight tools
• 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
• Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models
• 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

Requirements

• M.S. or Ph.D. degree in 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
• Experience in the autonomous vehicles domain, robotics, or complex simulation environments
• 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

Qualifications

• 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure
• 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

Skills

• 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

Benefits

Waymo offers a comprehensive benefits package including a discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Employees are also eligible to participate in Waymo’s benefits program.

Pay

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level.

Schedule

Full-time position.