Video Intelligence GenAI Research Intern

Meta; Bellevue, Menlo Park (WA, CA); On-site; USD 55,000 - 83,000; Full time

Job summary

Meta's Video Intelligence team is seeking motivated Research Interns to advance state-of-the-art video generation and understanding. You will work with experts in computer vision, generative modeling, and multimodal learning on frontier research projects.

Ideal candidates are PhD students in CS/EE with a focus on generative modeling, CV, or ML, proficient in Python and PyTorch, and experienced with diffusion models, LLMs, or autoregressive transformers.

Qualifications

• Currently pursuing a Ph.D. in CS, EE, or related field with a focus on generative modeling, CV, or ML.
• Proven track record of solid research achievements (grants, fellowships, patents, publications).
• Strong programming in Python and hands-on experience with deep learning frameworks like PyTorch.
• Experience with generative models (diffusion, LLMs, autoregressive transformers).
• Must have work authorization in the country of employment and plan to return to degree program after internship.

Responsibilities

• Collaborate with leading experts in CV, generative modeling, and multimodal learning.
• Contribute to research projects at the frontier of video generation models.
• Communicate results and insights to cross-functional teams and document findings.

Skills

Python programming

PyTorch

Generative modeling

Computer vision

Multimodal learning

Cross-functional communication

Research analysis

Education

PhD in Computer Science or Electrical Engineering

Tools

PyTorch

Diffusion models

LLMs

Autoregressive transformers

Job description

Meta's Video Intelligence team is seeking motivated Research Interns to advance state-of-the-art video generation and understanding. You will work with experts in computer vision, generative modeling, and multimodal learning on frontier research projects.

Ideal candidates are PhD students in CS/EE with a focus on generative modeling, CV, or ML, proficient in Python and PyTorch, and experienced with diffusion models, LLMs, or autoregressive transformers.