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# Staff Machine Learning Engineer, Tech Lead Active Learning

- **Company:** Waymo LLC
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $251,000.0 - $310,000.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Computer Programming, Python (Programming Language), Machine Learning, Software Engineering, Data Pipelines, Data Selection
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c868a9db8726fe78

## About the Role

* 8+ years of professional experience in the field of software engineering
 * Experience programming in C++ or Python
 * Experience building scalable, effective data pipelines including data materialization and bulk model inference.
 * Experience with ML model development.
 * Experience applying ML solutions to products.
 * Passionate about development efficiency and productivity.

We prefer:

 * Experience building internal tooling for ML developers

## Description

The Active Learning team builds tools and infra that empower all ML Engineers at Waymo to manage their ML datasets to efficiently train high quality models. These systems include solutions for data mining, data labeling, data understanding, model evaluation, model inference, model calibration, and dataset management. We are looking for a Technical Lead to join our team to apply the latest techniques in training / evaluation dataset management.

In this hybrid role, you will report to a Technical lead Manager, Staff Software Engineer.

You will:

 * Collaborate with the perception, planner, simulation and research teams to understand their modeling needs to shape solutions to improve dataset quality both in terms of distribution, label representation and quality.
 * Partner with various Waymo infrastructure teams to provide a golden path to do data selection.
 * Apply state-of-the-art techniques in data selection to reduce the amount of logged data that models at Waymo needs to train on.
 * Develop ML models to automatically detect human label quality issues and send them in for correction.
 * Build tooling to allow ML Engineers to experiment with signals that will be deployed to do log collection and label eval.
 * Deploy automated flywheels to automatically improve the datasets across all relevant dimensions.


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