Staff Machine Learning Engineer, Model Optimization

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 ML Platform team at Waymo provides a set of tools and technologies to support and automate the lifecycle of the machine learning workflow, including feature and experiment management, model development, debugging & evaluation, optimization, deployment, and monitoring. We are looking for a staff engineer with model optimization expertise to help us improve compute performance on our car. You'll work across the entire ML stack from models, to ML frameworks/libraries, to different HW platforms. You will collaborate with world-class scientists and engineers in Waymo, Google, Deepmind and will be pleasantly challenged with the state-of-art-model compression technologies.

In this role you'll:

• Lead the collaboration with world-class Waymo ML scientists in perception, planner, research and simulation. Build productive relationships and understand their models. Identify opportunities in both systems and models to make ML workloads faster.
• Lead projects from proposals, through goals and execution, to results. Lead and mentor junior engineers.
• Deep dive into the full stack of ML software stack. Analyze the ML workload performance. Apply model optimization, efficient deep learning techniques and ML software improvements.
• Collaborate on foundation models and ML System with external partners such as CoreML, Google Brain and Deepmind.

At a minimum, we'd like you to have:

• M.S. in CS, EE, Deep Learning or a related field
• 2+ years of experience as a technical lead
• 3+ years of experience on model optimization or efficient deep learning techniques
• Strong Python or C++ programming skills
• Thorough understanding of key ML system challenges and trade-offs.
• Solid experience with designing, training and debugging deep learning models to achieve the highest scores/accuracies.

It's preferred if you have:

• PhD in CS, EE, Deep Learning or a related field.
• Proven track record on efficient deep learning and/or model optimization techniques with foundation models.
• Deep knowledge on system performance, GPU optimization or ML compiler.
• 5+ years of experience on model optimization or efficient deep learning techniques