- Robot type
- Autonomous Vehicle
- Location
- San FranciscoCaliforniaUSA
- Job type
- Artificial Intelligence
- Posted
- Sep 1, 2026
- Salary
- $298,000–$368,000 a year
Sr Staff Tech Lead, Machine Learning Engineer, Perception
Job description
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.
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…
Job responsibilities
- 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.
Job requirements
- 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.
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