The Perception Team at Waymo builds the system that "sees" the world around the self-driving car. We conduct novel research to address real-world perception problems and collaborate with research teams at Alphabet. Here at Waymo, we have access to millions of miles of driving data from a diverse set of sensors, enabling researchers like you to develop complex models and techniques at scale. Improvements deployed to our system can immediately advance our large fleet of autonomous vehicles.

Within Perception, the Sensor Health team's job is to make sure that sensors "just work" for the entire self-driving car software stack. We make sure that all sensors are properly calibrated and consistently monitored at all times. We process data from next-generation sensors on next-generation vehicle platforms and work closely with both hardware and software teams to provide the best possible sensor data from our sensors to our upstream customers. To this end, we develop sensor data alignment and calibration algorithms, a growing sensor health backend, and deploy our systems both into the Waymo Driver and our log processing backend.

In this hybrid remote/in-office role, you will report to a Senior Staff TLM.

You will:

• Lead and build a team of ML engineers in charge of reliable sensing anomaly detection and calibration
• Build a data flywheel to automatically generate suitable training data for rare instance sensor degradation events using VLMs
• Own the entire sensor validation stack end to end
• Drive and accelerate collaborations between perception, system engineering, evaluation, and Alphabet teams

You have:

• MS in Computer Science, Robotics, Math, Physics or equivalent industry experience
• 6+ years of experience in designing, training, and deploying ML-driven real-time robotics systems
• 4+ years of experience leading technical teams and setting technical directions
• 2+ years of people management experience

We prefer:

• PhD in Computer Science, Robotics, Math, Physics, or a related technical field
• Experience in robotics, autonomous systems, or related automotive industries.
• Experience with various sensor modalities (e.g., LiDAR, radar, cameras) and their unique challenges.
• Familiarity with early fusion multi modal sensor models and their development challenges
• Experience with rare instance or zero-shot detection problems