Staff Machine Learning Engineer, TLM , Labeling Automation

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 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.

Nice To Have:

• Experience in building internal tooling for ML developers

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. 

Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.