Waymo is an autonomous driving technology company with the mission to be the 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 One, a fully autonomous ride-hailing service, and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over one million rider-only trips, enabled by its experience autonomously driving tens of millions of miles on public roads and tens of billions in simulation across 13+ U.S. states.

The Waymo ML Infrastructure team works with Research and Production teams to develop models in Perception and Planning that are core to our autonomous driving software. We ensure our partners by offering the best solutions for the entire model development lifecycle. These solutions are developed in close collaboration with teams at Google. They are geared towards both scaling models and solving problems unique to ML for autonomous driving.

We develop a set of libraries and tools that enhance TensorFlow and JAX, and address scalability, reliability, and performance challenges faced by Waymo's ML practitioners: training fast and at scale, increasing ML accelerator efficiency, fine-tuning multimodal LLMs for autonomous driving tasks, discovering hyper-parameters, retraining neural networks, computing reliable and noiseless metrics on validation sets, and validating newly trained DNNs when deployed into the full onboard software stack.

In this hybrid role, you will report to the Technical Lead Manager of Machine Learning Training.

You Will

Develop the infrastructure components necessary for distributed training, including job scheduling, resource management, data distribution, and model synchronization.
Implement automation solutions for provisioning, deployment, monitoring, and scaling of distributed training infrastructure to improve operations and reliability.
Monitor system health, diagnose and troubleshoot issues, and perform routine maintenance tasks to ensure the reliability of the distributed training infrastructure.
Identify performance bottlenecks and optimization opportunities
Improve the developer experience and performance of our scalable ML framework

You Have

Bachelor's degree in Computer Science, Engineering, or related field, or 2+ years equivalent experience
Experience with distributed systems principles and experience building distributed systems for production environments.
Solid Python or C++ skills
Prior experience with Machine Learning frameworks (e.g., TensorFlow, PyTorch) and distributed training algorithms
Debug complex distributed systems issues
Experience communicating updates and resolutions to customers and other partners

We Prefer

Practical familiarity using ML accelerator profiling tools to uncover performance bottlenecks
Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP) and experience deploying and managing distributed systems in cloud environments
Knowledge of optimization and deep learning algorithms

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.

Salary Range

$158,000—$200,000 USD

Waymo

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Machine Learning Engineer, Training