Machine Learning Engineer, Perception

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.

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.

Our goal is to improve the Waymo Driver performance by owning the end-to-end driving behavior for various onroad problems clusters. We work with upstream and the downstream teams on developing the ML models and integrating into the Waymo Driver. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware.

In this hybrid role, you will report to a Technical Lead Manager.

You will:

• Own tasks in the ML Driver, take responsibility for task performance, create ML methods and recipes to scale and improve tasks.
• Analyze behavior of ML systems in real-world application, identify issues and root causes, advise or develop short- and long-term solutions.
• Develop methods and recipes for distributed fine-tuning enabling multiple developers to simultaneously improve the model, develop methods and recipes to avoid regression against a production system.
• Monitor ML systems in production, develop methods for automatically detecting issues or regressions, develop AI-aided analysis and debugging tooling.
• Develop and maintain model evaluation recipes and metrics for measuring and improving performance of pre-trained and fine-tuned models

You have:

• Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience
• 3+ years experience in Machine Learning and/or Computer Vision
• Experience with Python or C++
• Experience with ML frameworks like PyTorch or JAX

We prefer:

• MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline
• Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI

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.