Job description

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 Roadgraph team builds the intelligent platform and automated pipelines that create, validate, and scale the high-definition geo knowledge base powering the Waymo Driver. We combine applied machine learning, computer vision, statistical evaluation, and large-scale distributed systems to solve one of the most foundational and exciting challenges in autonomous driving. High-definition mapping is a foundational pillar enabling the Waymo Driver to navigate safely, smoothly, and reliably. To rapidly expand commercial autonomous ride-hailing to new cities and continents, we must scale our automated mapping pipelines. This role sits at the center of that mission—building the applied ML models, evaluation engines, and large-scale data infrastructure that transform real-world sensor streams into the world's most precise and reliable spatial knowledge base. We work closely with onboard engineers to solve problems in an end to end fashion. You will be working on all aspects of the modeling life cycle (labeling, training, evaluation, inference), put your models to work, and measure impact on the overall system. You will: Evaluate and launch machine learning models that automatically generate map from raw data. Work with onboard teams to evolve Waymo’s mapping format to make it more automation friendly. Apply statistical analysis to validate large-scale changes. Build backend infrastructure for storing and processing many forms of map data. Scale our systems to support mapping the whole world. You have: B.S. in Computer Science or a similar technical field, or equivalent practical experience. Hands-on experience with statistical analysis, experiment design, and quantitative evaluation of ML models or data pipelines. Experience building scalable data processing pipelines using distributed frameworks like Google Data Flow or Apache Beam. Demonstrated ability to take ownership of complex technical problems end-to-end and deliver measurable results in a collaborative team environment. A passion for good software engineering. We prefer: Master’s or Ph.D. in Computer Science, Machine Learning, Robotics, Computer Vision, or a related quantitative field. Experience with high-performance computing, C++ optimization, and large-scale distributed systems architecture. Domain familiarity with autonomous vehicles, robotics, high-definition (HD) mapping or GIS/spatial databases. #LI-Hybrid 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 the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 — $263,000 USD

Requirements

Must-haves

Evaluate and launch machine learning models that automatically generate map from raw data.
Work with onboard teams to evolve Waymo’s mapping format to make it more automation friendly.
Apply statistical analysis to validate large-scale changes.
Build backend infrastructure for storing and processing many forms of map data.
Scale our systems to support mapping the whole world.
B.S. in Computer Science or a similar technical field, or equivalent practical experience.
Hands-on experience with statistical analysis, experiment design, and quantitative evaluation of ML models or data pipelines.
Experience building scalable data processing pipelines using distributed frameworks like Google Data Flow or Apache Beam.
Demonstrated ability to take ownership of complex technical problems end-to-end and deliver measurable results in a collaborative team environment.
A passion for good software engineering.
Master’s or Ph.D. in Computer Science, Machine Learning, Robotics, Computer Vision, or a related quantitative field.
Experience with high-performance computing, C++ optimization, and large-scale distributed systems architecture.
Domain familiarity with autonomous vehicles, robotics, high-definition (HD) mapping or GIS/spatial databases.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningml

Platform: greenhouse
Effort: quick

Benefits

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Vision