Staff Machine Learning Engineer, TLM , Labeling Automation

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.

• 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.

• 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 the entire lifecycle—from research and experimentation to robust, scaled production deployment.
• 2+ years of experience in direct people management, technical leadership, or leading engineering teams as a TL/TLM.

• Experience in building internal tooling for ML developers