2027 Summer Intern, PhD, Machine Learning, Computer Vision

Analyze and characterize internal feature representations of deep multimodal perception foundation models.

Design and implement active learning and intelligent data curation algorithms to identify rare, safety-critical edge cases.

Explore automated data labeling and validation workflows leveraging modern Vision-Language Models (VLMs).

Train, fine-tune, and evaluate deep neural networks to improve model performance.

Validate model performance on large-scale autonomous vehicle sensor datasets and simulation environments.

Currently enrolled in a PhD program in Computer Science, Robotics, Electrical Engineering, or a related quantitative field.

Experience programming in Python and C++.

Practical experience training, fine-tuning, and evaluating deep learning models for computer vision or multimodal perception (e.g., using PyTorch, JAX, or TensorFlow).

Solid understanding of modern neural architectures (e.g., Vision Transformers, multi modal sensor encoders).

Research experience or publications in active learning, self-supervised representation learning, core-set selection, or multi modal foundation models (VLMs).

Hands-on experience working with multi-camera or 3D LiDAR perception systems in robotics or autonomous driving.

Experience building VLM evaluation/judge pipelines or large-scale data mining workflows.

Familiarity with high-dimensional feature analysis and clustering techniques.

Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.