About the role
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. The Architecture & ML Integration (AMI) team sits within SIML and focuses on horizontal, cross-cutting challenges spanning Waymo's end-to-end system. Our mission is to enable rapid evolution towards an ML-first architecture that is modular, flexible, and easy to experiment with. We do this by improving onboard software architecture, system debuggability, and developer experience through better API designs and comprehensive pipelines. The AMI team drives the convergence of Planner and Perception systems and tackles critical shared infrastructure components You will: Have 8+ years of professional software development experience on large scale products Lead the software architecture and infrastructure development for onboard model consolidation across many onboard subsystems. Design and build an onboard framework to support flexible and efficient model integration and inference in Waymo’s onboard and simulation environments. Spearhead the alignment and unification of model’s data generation subsystem between model training data preparation and model inference. Develop Simulation solutions to enable robust testing and efficient evaluation/validation to enable good developer experience for consolidated model development. Embedded deeply with model teams to drive the productionization of unified models. Establish new software development best practices and technologies to accelerate model development and releases. You have: BS/MS in Comp Sci, EE, Robotics, Physics, Math, or related field (or equivalent experience) Extensive experience designing and building large-scale, high-performance C++ software architecture for mission-critical systems or ML inference. Proven track record of leading complex, cross-functional engineering projects as a technical lead. Deep understanding of ML deployment, inference frameworks, and associated developer tooling. Good communication and collaboration skills working with cross-org partner teams We prefer: Master's or PhD in Computer Science, Machine Learning, Robotics, or a related field. Experience with data pipelines and feature extraction for machine learning models. Prior experience in the autonomous driving industry, particularly focusing on onboard compute, perception, or planning systems. Experience with hardware accelerators (e.g., TPUs, GPUs) and ML models inference in edge/onboard environments. 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