Research Engineering Intern

About the Role

You'll work with our research team on computer vision, representation learning, and how what a system sees turns into what it remembers. Current focus areas include HyperGNN encoders, JEPA-style predictive learning, self-supervised learning, and Bayesian methods for handling uncertainty. There's also room to work on VLA models and real sensors or robots. Your job is to take ideas, build them, and find out honestly whether they work.

Key Responsibilities

• Build and evaluate perception models for images, video and multimodal data
• Try predictive and self-supervised approaches, including JEPA-style models
• Use Bayesian and probabilistic methods when data is noisy or incomplete
• Connect perception to memory systems to track relationships, events, and change over time
• Work with VLA models and integrate sensors or robots as needed
• Reproduce papers, set baselines, and design experiments
• Write clean research code and document results

Requirements & Tech Stack

• Strong Python and PyTorch, including training and evaluating models
• Real depth in at least one of: computer vision, representation learning, probabilistic ML, graph neural networks, or embodied AI
• Solid ML fundamentals
• Ability to explain model behavior
• Track record of taking an idea from a paper to a working, evaluated implementation
• Work examples can include: papers, open-source contributions, projects, or shipped systems
• Initiative, honesty about results, and clear explanations of choices

Qualifications

• 2–5 years of hands-on work in research, industry, or substantial independent projects
• Nice to have:
• JEPA, self-supervised learning, or world models
• Bayesian learning or uncertainty estimation
• Graph-based or structured representations
• Video, temporal, or multimodal models
• VLA models, robot learning, or hardware integration