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

Job description

Job responsibilities

• Designing and Building Machine Learning Models: Writing clean, high-performance code to implement core algorithms for entity-centric lane geometry detection and relational topology decoding (e.g., merges, splits,…
• Running Experiments and Training Pipelines: Setting up data pipelines and training neural networks across vehicle sensor modalities and map priors, leveraging techniques like proxy auto-encoding and prior-dropout to…
• Benchmarking and Analyzing Performance: Creating structured evaluation metrics to benchmark model accuracy and topological correctness across complex intersections, analyzing failure cases, and iterating on…
• Cross-Functional Collaboration: Partnering closely with research mentors, buddy, and upstream/downstream engineering teams to evaluate downstream planning impact and package insights for publication or internal…

Job requirements

• Currently pursuing a Master's or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related quantitative discipline.
• Strong programming proficiency in Python and solid experience with modern deep learning frameworks (e.g., PyTorch, JAX, or TensorFlow).
• Hands-on experience designing, training, and debugging deep learning architectures for Computer Vision, 3D Perception, or Graph Neural Networks (e.g., Transformers, DETR-based detectors, GNNs, or BEV perception).
• Solid foundational knowledge of 2D/3D geometry, coordinate transformations, and spatial/relational reasoning.