Software Engineer, Operational/ Process Efficiency

<div class="content-intro"><p>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.</p></div><p>This role is at the intersection of robotics and machine learning, driving the next generation of operational efficiency for Waymo's rapidly expanding autonomous fleet. You will lead efforts to generalize complex depot operations—such as exterior cleaning, sensor calibration, and maintenance checks—using advanced robotics. Key work involves leveraging foundation models, reinforcement learning, simulation, and integrating ML models in production at scale. You will interface closely with operations teams to translate real-world needs into robust, working solutions.</p> <p><em>This role follows a hybrid work schedule and reports to a Director, Hardware and Sensors.</em></p> <p><strong>You will:</strong></p> <ul> <li style="line-height: 1.5;">Drive the automation of the hardware lifecycle for critical sensors (lidar, radar, cameras) and compute modules.</li> <li style="line-height: 1.5;">Develop and deploy agentic systems and foundation models to streamline workflows between internal teams and contract manufacturers.</li> <li style="line-height: 1.5;">Identify opportunities to apply AI to manufacturing, installation, and troubleshooting processes to increase operational velocity.</li> <li style="line-height: 1.5;">Interface with a diverse set of stakeholders, including hardware design engineers, failure analysis engineers, and diagnostic teams, to translate physical requirements into technical specifications.</li> <li style="line-height: 1.5;">Bridge the gap between experimental ML models and high-scale production environments.</li> </ul> <p><strong>You have:</strong></p> <ul> <li style="line-height: 1.5;">A Masters or PhD in Machine Learning, Computer Science, or a related technical field.</li> <li style="line-height: 1.5;">A proven track record of delivering working engineering solutions, balancing scientific rigor with production needs.</li> <li style="line-height: 1.5;">Experience in training, evaluating,