Machine Learning Engineer, Labeled Data

About the position

Waymo is an autonomous driving technology company with the mission to be the 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. The Labeling Platform Team creates data solutions to power groundbreaking research and development during all stages of model training: pretraining, supervised fine-tuning, reinforcement learning, etc. The labeled data that the team produces is used to directly enhance and evaluate the Waymo Driver as well as the vast variety of models that power other parts of the business.

Responsibilities

• Build a Data Flywheel: Design and implement systems that continuously improve the Waymo Driver.
• Collaborate with ML practitioners on models for perception, behavior prediction and planner to understand their models and help them define data requirement.
• Develop automation to both produce the labels and/or find problematic label issues within the already produced labeled datasets.
• Build large scale distributed systems that index and serve the large amount of labeled data to model training pipelines.

Requirements

• Bachelor's degree in Computer Science, Engineering, or related field, and 2+ years equivalent experience
• Experience in producing and managing large scale ML training datasets
• ML Model development experience
• Experience in programming C++ or Python. Bonus if you have Typescript experience.

Nice-to-haves

• Experience in developing human + ML interaction schemes to boost productivity and quality of label generation.
• Experiencing programming using Typescript.
• Hands-on experience with LLM/GenAI-based products.
• Experience using open source computer vision models such as YOLO and SAM.

Benefits

• Discretionary annual bonus program
• Equity incentive plan
• Generous Company benefits program