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# Machine Learning Engineer, Fleet Monitoring & Response

- **Company:** Waymo LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $175,000.0 - $215,000.0
- **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, Marketplace, Data Pipelines
- **Published:** August 11, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/87206015/1

## About the Role

* BS degree in Computer Science or equivalent practical experience.
 * 5+ 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.


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