As a Staff Software Engineer (L6) on the Labeling Team, you will lead the technical strategy for automating our data pipelines. 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 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.

In this hybrid role, you will report to a Technical lead Manager, Staff Software Engineer.

You will:

• Architect and Scale Auto-Labeling: Lead the design and deployment of highly scalable auto-labeling pipelines that significantly improve data throughput and reduce our reliance on 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 Technical Direction: Act as a technical pillar for the Labeling organization. Set the long-term ML strategy, guide architectural decisions, and mentor senior and mid-level engineers.
• 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.

You have:

• 8+ years of professional experience in the field of software engineering and applied machine learning
• Experience programming in C++ or Python
• Experience building, evaluating, and deploying deep learning models for object detection, segmentation, and spatial tracking
• Experience in large model training, distributed computing, and scaling deep learning architectures using frameworks like PyTorch or TensorFlow
• Experience taking machine learning solutions through the entire lifecycle—from research and experimentation to robust, scaled production deployment

We prefer:

• Experience building internal tooling for ML developers

#LI-hybrid