Senior Software Engineer, Machine Learning Performance

Waymo is an autonomous driving technology company with the mission to be the world's 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’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Waymo's Compute Team is tasked with a critical and exciting mission: We deliver the compute platform responsible for running the fully autonomous vehicle’s software stack. To achieve our mission, we architect and create high-performance custom silicon; we develop system-level compute architectures that push the boundaries of performance, power, and latency; and we collaborate closely with many other teammates to ensure we design and optimize hardware and software for maximum performance. We are a multidisciplinary team seeking curious and talented teammates to work on one of the world’s highest performance automotive compute platforms. This role follows a hybrid work schedule, and you will report to the Tech Lead Manager of the Machine Learning Performance team. You will: • Act as Technical Lead (TL) for a small team (~5 engineers) focusing on individual ML model performance optimization and delivering maintainable, high-quality solutions. • Set the technical vision, direction, and multi-quarter roadmap for ML performance optimization within the team's scope • Drive and coordinate large, open-ended technical projects that span multiple application and infrastructure teams, ensuring timely and robust delivery • Collect, trace, and analyze application/ML model performance for complex optimization opportunities, and prototype/generalize solutions at the application, compiler, or infrastructure level (firmware, runtime, framework) • Influence and motivate infrastructure teams (e.g., compiler) to prioritize and land performance-critical optimizations, setting clear expectations using solid methodology (e.g., roofline) • Provide technical mentorship and guidance to team members, helping them to grow and ensuring technical excellence and code health across the team's deliverables You have: • MS degree in Computer Science/Electrical Engineering or equivalent, or equivalent practical experience • 5+ years of experience writing complex C++ code • 5+ years of experience writing code in Python • 7+ years experience in optimizing compute performance for ML applications • Experience in compute architectures, performance analysis, and optimization methodologies • Communicate well to cross-functional teams and effectively mitigate timezone differences We prefer: • Experience in ML modeling and also ML compiler implementation • Experience in performance tools, simulators, and HW/SW codesign • Proficiency in building and nurturing effective collaborations across application teams and infrastructure teams • Experience with pruning, quantization, and other model performance optimization techniques …