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

What you'll do

• Implement high-performance algorithms for lane geometry and topology understanding.
• Build data pipelines and train neural networks using vehicle sensor modalities and map priors.
• Collaborate with research and engineering teams to assess planning impact and prepare insights for publication or deployment.

What they ask for

• Currently pursuing a PhD in Computer Vision, Robotics, Electrical Engineering, Machine Learning, or a related quantitative discipline.
• Proficiency in Python and experience with modern deep-learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience designing, training, and debugging deep-learning architectures for Computer Vision, 3D Perception, or Graph Neural Networks.
• Foundational knowledge of 2D/3D geometry, coordinate transformations, and spatial/relational reasoning.

Nice to have

• Publications at top-tier machine-learning, computer-vision, or robotics conferences.
• Experience with vectorized HD map learning, lane topology estimation, dynamic roadgraph modeling, distributed training, data infrastructure, or autonomous-vehicle perception and sensor fusion.

Pay

$85 — $85 USD (hourly pay)

Tools

Python, PyTorch, TensorFlow

AI focus

Develop and train deep-learning models for road understanding, including lane geometry and topology, using vehicle sensor modalities and map priors.