2027 Summer Intern, MS/PhD, Weather Perception

Waymo•San Francisco, CA

8d•Hybrid

About The Position

Waymo’s Weather and Degraded Roads Perception team develops features that help the Waymo Driver operate safely and comfortably in rain, fog, snow, ice, potholes, unpaved roads, trenches, and construction sites. The team collaborates with planning and control teams to define vehicle behavior in these conditions. Its work includes designing perception features, developing and retraining machine-learning models, building hybrid onboard/offboard systems to coordinate fleetwide behavior, and using Vision Language Models to address long-tail situations. The intern will research and implement machine-learning perception solutions for Waymo’s self-driving stack, develop applied research solutions to data-scarce challenges in weather perception, and participate in cross-functional discussion and development with the Waymo onboard software team. The position is a hybrid onsite internship. Applications are accepted on a rolling basis until the role is filled.

Requirements

Currently pursuing an MS or PhD in Computer Science, Robotics, or an equivalent field.
Experience with machine-learning applications, including transformer applications and machine-learning evaluation techniques.
Experience with Python programming.
Experience reading and implementing machine-learning algorithms from academic papers.
Familiarity with TensorFlow, PyTorch, or JAX.

Nice To Haves

Classical computer vision or computational geometry experience.
Submissions to relevant robotics, machine-learning, or computer vision conferences, such as ICML, RSS, IROS, CoRL, or CVPR.
Experience with real-time robotic systems.
Experience with C++ programming.

Responsibilities

Research and implement novel machine-learning perception solutions for Waymo’s self-driving stack.
Develop applied research solutions to data-scarce challenges in Waymo’s weather space.
Participate in cross-functional discussion and development within the Waymo onboard software team.