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 help 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 build 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, re-training 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 role, you'll:

• Report into the Head of ML Training
• Design distributed systems tailored for machine learning workloads of different sizes, considering factors such as scalability, fault tolerance, and resource use
• Optimize system performance by identifying bottlenecks and implementing efficient algorithms for distributed training
• Increase training efficiency of different neural network architectures
• Improve the developer experience and performance of our scalable ML framework
• Collaborate with machine learning engineers and other partners to understand their requirements and provide infrastructure support for their experiments and projects.

At a minimum we'd like you to have:

• BS in Computer Science, Math, or 5+ years equivalent real-world experience
• Python or C++ industry experience
• Prior experience with Machine Learning frameworks (e.g., TensorFlow, PyTorch) and distributed training algorithms
• Practical familiarity using ML accelerator profiling tools to uncover performance bottlenecks
• Understand ML compiler infrastructure, such as of HLO and MLIR
• Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP) and experience deploying and managing distributed systems in cloud environments

It's preferred if you have:

• MS in Computer Science, Math
• Prior work scaling model training to multiple accelerators
• Prior experience on ML compiler optimization such as TVM or XLA
• Knowledge of optimization and deep learning algorithms

#LI-Hybrid

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.