Staff Tech Lead Manager, Machine Learning, Simulator 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.

About the Role

Waymo’s simulator is one of the most complex virtual environments ever built, blending deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver. Within this cutting-edge ecosystem, the Simulator Evaluation team faces the ultimate data challenge: How do you mathematically prove that a virtual world is "real"?

While our team programmatically builds the metrics, pipelines, and ML systems that grade this hybrid environment, your challenge as a Staff Tech Lead Manager is to scale both this technology and the highly specialized team executing this vision. In this role, you will bridge technical vision and execution by translating cutting-edge generative AI advancements into production-ready evaluation tools used by engineering teams across Waymo to ensure the safety and performance of our autonomous fleet, while fostering an inclusive engineering culture of extreme technical rigor, rapid experimentation, and robust software quality.

You will:

• Lead a top-tier applied ML team focused on building eval frameworks, infrastructure, and AI-powered tooling for World Model understanding and evaluation.
• Drive technical direction, and provide technical inputs and guidance to the team.
• Architect ML-driven metrics and evaluation frameworks that rigorously test World Model quality, automatically surfacing anomalies and edge cases across vast datasets.
• Chart the technical direction for World Model evaluation, working closely with Product and Program managers to translate modeling challenges into clear business objectives.
• Build AI-powered internal tools and products that streamline the model eval journey for users, transforming complex model analysis into intuitive, automated, fast-iteration workflows.
• Partner with cross-functional teams (e.g., Onboard, Research, and other Simulation teams) and senior technical leadership to deploy your team’s solutions into Waymo’s production-critical systems.

You have:

• Degree in Computer Science, Robotics, Machine Learning, a related technical field, or equivalent practical experience
• 3+ years of hands-on technical leadership experience, directly managing and guiding high-performing engineering teams of 5-10 people
• 7+ years of experience building, deploying, and optimizing machine learning systems in production environments
• Strong software engineering proficiency in Python and/or C++
• Experience with ML frameworks like PyTorch, JAX, or Tensorflow, backed by a solid foundation in deep learning, transformers, and large-scale model deployment
• Proven ability to design robust evaluation frameworks for complex systems, combined with strong analytical skills and proficiency in large-scale data processing

We Prefer:

• M.S. or PhD. in Computer Science, Robotics, Machine Learning, or a related quantitative field.
• A proven track record of training and/or evaluating large-scale generative models (e.g., reinforcement learning, diffusion, world models, 3D generative models, or video generation)
• Experience training and optimizing models on massive GPU/TPU clusters, and managing large-scale data processing/MLOps systems
• Experience building and deploying AI-powered products or internal developer tools that streamline or automate complex workflows

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. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.