Senior Machine Learning Engineer, Computer Vision/VLM

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 DriverTM—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.

In Semantics, our team's mission is to create the highest-fidelity, most comprehensive offboard perception autolabels at a massive scale, serving as the foundation for training and validating the AV stack.

We are an advanced ML and engineering team that leverages state-of-the-art computer vision, deep learning, and generative AI to automatically analyze driving logs, generate rich scene understanding, and power the data engine that enables Waymo to scale safely and efficiently.

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

You will:

• Develop and train state-of-the-art computer vision / multimodal models (e.g., Gemini) to extract the rich semantic information (e.g., object attributes, scene properties, interaction dynamics) required by the AI agent.
• Design and implement a scalable AI agent framework that integrates large foundation models (e.g., Gemini) with the outputs of our perception models and internal knowledge bases.
• Develop and apply Fine-tuning and Reinforcement Learning (RL) techniques to create a "data flywheel," continuously improving the system's captioning and reasoning abilities through automated feedback.
• Develop and prototype novel prompting strategies for Vision-Language Models (VLMs) to elicit complex, causal reasoning about driving scenarios.
• Collaborate closely with the ML Infra, Perception, Behavior, and AI Foundation teams to define data requirements and integrate the captioning system into the broader ML development lifecycle.
• Own the full system lifecycle, from advanced model development and prototyping to production deployment and scaling for massive data generation