Senior Machine Learning Engineer, Fleet Monitoring & Response

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.

You will:

• Design, scale, and optimize Waymo's real-time Fleet Monitoring and event response engine to support expansion to global operating locations.
• Develop and deploy spatial-temporal anomaly detection models (e.g., S2-cell statistical regressions) and leverage Multimodal Foundation Models (Gemini/VLMs) to detect, triage, and automatically respond to off-nominal operations.
• Build and standardize the ML infrastructure for fleet monitoring models, including automated training/inference pipelines, low-latency spatial data stores (e.g., in-memory S2 grids), and continuous model drift monitoring.
• Partner with Product Data Scientists to productionize, evaluate, and scale experimental models, translating notebooks and prototype algorithms into production-grade systems.

You have:

• BS degree in Computer Science or equivalent practical experience.
• 6+ years of experience programming in backend coding languages such as Java or C++.
• Experience in building backend platforms supporting multiple product use-cases/services.
• Prior Machine Learning Engineering experience in Python using mature ML frameworks such as TensorFlow, PyTorch or Keras.

We prefer:

• MS in Computer Science, or equivalent practical experience.
• Experience building and deploying ML / Optimization models into production environments.
• Experience developing ML data pipelines and ML workflow automation code on top of a mature ML infra.
• Experience working at another Ride hailing or Marketplace company.
• Coursework background in ML and Optimization.

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.