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

#LI-Hybrid