Machine Learning Engineer

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.

This role follows a hybrid work schedule and reports to a Software Tech Lead Manager.

You will:

• Own and drive the roadmap for ML-powered pickup and dropoff features, from problem definition through launch, in partnership with PM, UX, data science, and other commercialization engineering teams

• Train, evaluate, and deploy ML models for occupancy detection, rider-vehicle interactions, pullover selection, and lost and found

• Design fallback systems that cascade between onboard and offboard models based on latency, confidence, and connectivity

• Build distributed systems that span vehicles, cloud services, and rider apps, and that handle intermittent and inconsistent connectivity well

• Work across the stack (data pipelines, model serving, backend services, and client integration) to ship end-to-end, user-facing features

• Define success metrics and use offline evaluation, simulation, and live experiments to measure impact on service quality and rider experience

You have:

• BS degree in Computer Science or equivalent practical experience

• 5+ years of software engineering experience, including building and deploying ML models into production

• Experience building backend distributed systems

We prefer:

• Experience with C++

• MS in Computer Science, or equivalent practical experience

• Experience in developing machine learning models, infrastructure, and pipelines

• Experience deploying models on-device or on edge hardware with compute and latency constraints

• Track record of owning ambiguous, user-facing problems end-to-end and setting your own technical direction

• Experience in autonomous vehicles or robotics