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# Senior Machine Learning Engineer, Fleet Monitoring & Response

- **Company:** Waymo LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $213,000.0 - $263,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), C++ (Programming Language), Computer Programming, Python (Programming Language), Machine Learning, Tensorflow, Spatial Data Infrastructures, Pytorch, Delivery Pipeline, Large Language Models, Backend, Keras, Information Technology, Machine Learning Operations, Google Gemini, Marketplace, Data Pipelines
- **Published:** October 30, 2026
- **Apply:** https://www.adzuna.com/details/5735022673

## About the Role

* 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.

## Description

* 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.