Senior Staff Tech Lead, Waymo

• BS or MS in Computer Science, Robotics, Machine Learning, or equivalent practical experience
• 8+ years of industry experience in Machine Learning, AI or Computer Vision
• Proven track record of architecting and shipping large-scale, production-grade machine learning models
• Expert proficiency in Python, C++, and modern ML frameworks (e.g., PyTorch, JAX, TensorFlow)
• Demonstrated experience leading large technical initiatives as a Tech Lead across cross-functional teams

Desirable

• The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the self-driving car, i.e., the system that “perceives” the world around the car
• We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet
• We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware
• In this hybrid role, you'll report to a Senior Staff Technical Lead Manager
• Set the long-term technical roadmap for next-generation multi-modal perception architectures, driving paradigm shifts in multi-task learning, spatial-temporal modeling, and scene representation
• Architect unified, scalable representations combining diverse sensor modalities across heterogeneous sensor suites
• Lead cross-functional initiatives across Perception and Planner and partner with hardware and infrastructure teams to optimize foundational architectures for onboard requirements
• Mentor ML engineers and foster an engineering culture of rapid prototyping and rigorous benchmarking