Staff Machine Learning Engineer, TLM , Labeling Automation

Start free →

Or apply yourself on the official page →

About the role

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. As a Staff Machine Learning Technical Lead Manager (TLM) on the Labeling Team, you will lead the technical strategy for automating our data pipelines while managing and growing a team of machine learning engineers. You will build cutting-edge auto-labeling systems to drastically scale our throughput and develop intelligent auto-graders to guarantee exceptional data quality. This is a high-impact leadership role where you will manage team members, train, deploy, and orchestrate state-of-the-art computer vision architectures and Vision-Language Models (VLMs) to solve complex semantic labeling challenges across our massive autonomous driving fleet. You Will: • 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. • 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. You Have: • B.S. in Computer Science, Machine Learning, Robotics, or a related technical field, or equivalent practical experience. • 7+ 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. • Must have: Extensive hands-on experience in large model training, distributed computing, and scaling deep learning architectures using frameworks like PyTorch or TensorFlow. • Strong coding proficiency in Python and/or C++, with a track record of writing maintainable, production-grade software. • Experience taking machine learning solutions through

View the official posting →