Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

The role involves owning a domain within scene understanding, leading technical roadmaps, managing ML engineering teams, and ensuring project delivery.

Develop and train computer vision and multimodal models, design scalable AI frameworks, and improve systems through fine-tuning and reinforcement learning techniques while collaborating with multiple teams.

The role involves defining the product vision for Perception ML workflows, leading the product lifecycle, and enhancing ML model development and performance through collaboration with engineering and research teams.

Lead design and verification of depot automation features, develop system architecture and qualification methods, debug field issues, extend features across vehicle platforms, and build monitoring and verification systems with cross-functional teams.

Lead design and execution of data selection, incremental model updates, deployment, and in-field monitoring for large-scale perception ML models. Build and grow teams, define technical roadmaps and OKRs, make staffing and technical decisions, and ensure reliable delivery for an ML system serving millions of rides.

Develop, scale, and monitor ML and perception systems for autonomous vehicles. Analyze real-world ML behavior, root-cause issues, create automated detection and debugging tools, and maintain safety-critical metrics.

Design and build foundation-scale datasets and training recipes; develop human and machine labeling methods; create continuous data intake and automated data collection/model update pipelines; evaluate real-world model performance and detect regressions; maintain ground-truth-free performance metrics.

Lead and grow a Vehicle Understanding team building production modules for fine-grained vehicle classification, states, and intents. Drive technical strategy, collaborate with cross-functional partners, optimize pipelines, and balance short- and long-term priorities to scale Waymo's autonomous driving capabilities.

Lead training and delivery of large-scale pre-trained perception models (image/audio), integrate encoders into downstream LLMs, own technical roadmaps and OKRs, hire and develop ML engineering teams, make staffing and technical decisions, and ensure production readiness including real-time system constraints.

Lead development and scaling of panoptic (instance + semantic) and 4D occupancy segmentation models. Collaborate on efficient architectures, labeling workflows, diagnostics, metrics, and deployment—using large pre-trained models and integrating with detection/tracking.