2027 Summer Intern, PhD, Perception/Road Understanding, ML Engineer

Highlights

• Build ML models for entity-centric lane geometry and topology decoding.
• Train networks across sensor and map data to handle construction and occlusions.
• Benchmark topological correctness at complex intersections to guide planning impact.

What they offer

• Eligible to participate in company benefits programs subject to requirements

Ideal candidate

A curious MS or PhD student passionate about autonomous driving and geometric learning who loves rigorous experimentation and cross-functional collaboration to turn road understanding research into real-world impact.

Requirements

• Currently pursuing a Master's or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related field
• Strong Python proficiency with experience in modern deep learning frameworks
• Hands-on experience designing, training, and debugging deep learning architectures for vision or 3D perception
• Solid foundation in 2D/3D geometry, coordinate transformations, and spatial reasoning