2027 Summer Intern, PhD, Machine Learning, Computer Vision

You will:

• 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.

You have:

• 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).

We prefer:

• 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.