Senior/Staff Machine Learning Engineer, Simulation Realism

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 Simulation Team at Waymo builds state-of-the-art simulations of realistic environments for testing and training the Waymo Driver. Our team is a diverse, and collaborative group of software engineers, machine learning (ML) engineers, and data scientists. We develop industry-leading simulation solutions using advanced ML algorithms that measure and enhance the performance of the Waymo Driver. By applying machine learning, we model the real world, including realistic agents (vehicles, pedestrians, cyclists, motorcyclists), roads, traffic control systems, and weather conditions.

A key aspect of our simulation effort is the generation of foundational data that enables realistic simulation and trustworthy evaluations. This involves producing high-fidelity world representations from complex, large-scale multi-modal sensor datasets. Key challenges we tackle include guaranteeing the highest levels of quality and accuracy in these datasets. We are also constantly innovating to build scalable and efficient systems to manage the sheer volume and intricacy of this data.

In this hybrid role, you will report to a Sr Staff TLM.

You will:

• Apply ML expertise to enhance the quality and accuracy of labeled data derived from multi-modal sensors.
• Develop and implement models to identify and correct issues in perception-based data for simulation.
• Analyze data quality, investigate anomalies, and drive improvements in our data labeling pipelines.
• Collaborate with perception and simulation teams to refine data requirements and quality standards.
• Lead efforts to address complex data quality challenges and define ML strategies for high-fidelity data generation.

You have:

• MS or BS in Computer Science or a related field.
• 5+ (L5) / 7+ (L6) years of experience in applied Deep Learning/Machine Learning.
• Strong background in computer vision, sensor fusion, or perception.
• Proficiency in Python and common ML frameworks (e.g., TensorFlow, PyTorch).
• Experience with data analysis and quality assessment.
• Strong coding and design skills.

We prefer:

• PhD degree in Computer Science or a similar discipline.
• Experience with autonomous vehicle sensor data.
• Familiarity with data labeling processes and challenges.
• Experience taking ML models from research to production.