About the position
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 One, a fully autonomous ride-hailing service, and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over one million rider-only trips, enabled by its experience autonomously driving tens of millions of miles on public roads and tens of billions in simulation across 13+ U.S. states. TaaS (Transportation as a Service) is responsible for building an amazing user experience and business around the Waymo Driver. We are responsible collectively for Growth, Territory Expansion, Service Quality (including pickup, dropoff and routing), Marketplace (pricing, positioning, matching), and Partnerships / B2B(2C) monetization. Marketplace team builds the core economic engine for Waymo’s commercialization services.
Responsibilities
- Implement and scale the core economic engine for Waymo's ride-hailing commercial services.
- Manage trip booking requests; match right vehicles to riders; calculate the price of rides; position Waymo’s vehicle for future demands.
- Develop ML infrastructure for automatically training and deploying sophisticated optimization and prediction ML models to make real-time decisions for pricing, matching, and positioning.
- Develop experimentation, monitoring and analysis infrastructure for analyzing ongoing deployed models.
Requirements
- BS degree in Computer Science or equivalent practical experience
- 2+ years of experience with developing large-scale infrastructure, distributed systems
- 2+ years of experience building productionized ML models.
Nice-to-haves
- Experience in C++
- MS in Computer Science, or equivalent practical experience
- 5+ years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Prior experience building and deploying various ML models such as reinforcement learning.
Benefits
- Discretionary annual bonus program
- Equity incentive plan
- Generous Company benefits program
