Research Intern, Generative AI & World Modeling

About the role

We are seeking a Research Intern with a strong foundation in statistics and applied mathematics to work alongside a diverse group of researchers. In this role, you will conduct fundamental research that bridges rigorous mathematical theory with state-of-the-art applications in machine learning, computer vision, computer graphics, and physical AI.

Responsibilities

• Fundamental Research: Leverage statistics, probability theory, and applied math to develop novel algorithms for machine learning (ML), computer vision (CV), and computer graphics (CG).
• Innovation & Prototyping: Translate core mathematical and statistical insights into scalable algorithms and open-source prototypes.
• Academic Impact: Publish groundbreaking work in top-tier machine learning, computer vision, and computer graphics conferences and journals (e.g., NeurIPS, ICML, CVPR, and SIGGRAPH).

Requirements

• Ph.D. candidate with a strong background in statistics, probability, continuous optimization, linear algebra, or differential equations, with a passion for applying fundamental theory to real-world problems
• Proven hands-on experience with SOTA machine learning algorithms, especially with generative machine learning and/or fine-tuning of LLMs/LVMs, and familiarity with ML implementation environments and platforms such as PyTorch and/or TensorFlow
• Deep knowledge and hands-on experience in computer vision and computer graphics are a plus
• Strong programming skills in Python or C / C++ for Windows and/or Linux are required
• Experience in working with open-source software frameworks is a plus
• Self-motivated, detail-oriented team player
• Good verbal and written communication skills

Pay

Duration of the job: 3-6 months