The opportunity
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…
What you'll do
Lead Technical Strategy & Architect Auto-Labeling: Define the technical vision and lead the deployment of highly scalable auto-labeling pipelines that significantly improve data throughput and eliminate manual annotation bottlenecks.
Build Robust Auto-Graders: Develop automated anomaly detection and quality evaluation systems (auto-graders) to assess annotation accuracy, detect regressions, and enforce rigorous quality standards across millions of labels.
Train & Deploy SOTA Computer Vision Models: Train, optimize, and push into production advanced 2D and 3D computer vision models. You will utilize architectures ranging from foundational zero-shot models like SAM (Segment Anything Model) and efficient real-time detectors like YOLO, to bespoke 3D perception and tracking models.
Leverage Vision-Language Models (VLMs): Fine-tune, and deploy large VLMs and LLMs, utilizing prompt optimization and advanced post-training techniques (SFT, RL, etc.), to solve complex, open-set labeling and contextual reasoning tasks.
Drive Organizational Technical Direction: Serve as a technical pillar for the Labeling organization. Establish long-term ML roadmaps, guide key architectural decisions, and elevate engineering standards across teams.
Collaborate Cross-Functionally: Work closely with Perception, Planner, and Simulation teams to align labeling capabilities with the evolving ML data needs of the Waymo Driver.
What they're looking for
- People Leadership & Management: Directly manage, mentor, and grow a team of high-performing ML engineers. Oversee career growth, performance evaluations, team hiring, and foster an inclusive, collaborative team culture.
- B.S. in Computer Science, Machine Learning, Robotics, or a related technical: field, or equivalent practical experience.
- + years of professional software engineering and applied machine learning experience.
- Must have: A deep background in computer vision, including proven experience: building, evaluating, and deploying deep learning models for object detection, segmentation, and spatial tracking.
