Research Scientist Intern, Generative AI (PhD)

Meta; Singapore; On-site; SGD 28,000 - 42,000; Full time

Job summary

Meta seeks Research Scientist Interns to join the Facebook team, working on generative AI technologies for video content and collaborating with researchers, engineers, and product partners to define problems, build solutions, and evaluate impact.

Internships run 12–16 or 24 weeks with multiple start dates. Applicants should be pursuing a Ph.D. in CS/EE/ML and have strong Python experience and DL framework knowledge.

Qualifications

• Pursuing a Ph.D. in CS, EE, ML or related field.
• Work authorization in country of employment at hire.
• Experience with Python and at least one systems/production language such as C++.
• Experience with deep learning frameworks like PyTorch or TensorFlow.

Responsibilities

• Collaborate with research scientists, software engineers and product partners.
• Define problems, build and evaluate solutions, and identify paths to product impact.
• Conduct experiments, analyze results, and communicate technical findings.

Skills

Python

Deep learning

Experiment design

Cross-functional communication

Education

PhD required

Tools

PyTorch

TensorFlow

C++

Job description

Meta was built to help people connect and share, and our technologies play a critical role in how people around the world communicate, create, and experience media. A research internship at Meta offers the opportunity to advance the state of the art while working on systems used across global products.

Meta is seeking Research Scientist Interns to join the Facebook team. We develop generative AI technologies that help people create, discover, and enjoy video content across Facebook.

Interns will work on one focused research problem at the intersection of generative models, AI agents, and recommendation systems. Potential areas include agentic systems for video generation; self-evolving agents; generative recommendations; and multimodal evaluation. Projects will be selected based on the intern's expertise and current team priorities.

The work combines research depth with real-world constraints such as quality, cost, latency, compute, scalability, robustness, and safety. Interns will collaborate with research scientists, software engineers, and product partners to define the problem, build and evaluate solutions, and identify a credible path from research result to product impact.

Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long, with various start dates throughout the year.

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Electrical Engineering, Machine Learning, or a related technical field
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
• Experience with Python and at least one systems or production programming language, such as C++
• Experience with deep learning frameworks such as PyTorch or TensorFlow
• Experience conducting research in machine learning, generative models, AI agents, multimodal learning, recommendation systems, or a closely related area
• Experience designing experiments, analyzing results, and communicating technical findings
• Intent to return to the degree program after completion of the internship or co-op
• Research achievements demonstrated through publications, patents, open-source contributions, fellowships, or other technical work
• Publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, RecSys, WWW, or related conferences and journals
• Research or project experience in one or more of the following areas: video or image generation, LLM-based agents, multi-agent systems, self-evolving agents, reinforcement learning, generative recommendation, or learned evaluation metrics
• Experience training, evaluating, or deploying large-scale generative or recommendation models
• Demonstrated software development experience through internships, research systems, production work, or widely used open-source contributions
• Experience working and communicating cross-functionally in a fast-paced research and engineering environment