Research Scientist Intern, Generative Recommendation (PhD)

Meta; Singapore; On-site; SGD 22,000 - 39,000; Full time

Job summary

Meta is seeking highly motivated Research Interns to advance the next generation of generative recommendation systems, combining LLMs, computer vision, diffusion models, and reinforcement learning to understand content and user preferences for personalized recommendations.

As a Research Intern, you will collaborate with researchers and engineers to explore new approaches to personalized retrieval, ranking, and generation of recommendation lists.

Qualifications

• Pursuing or holding a PhD in CS, EE, or related ML field.
• Proficient in Python and PyTorch.
• Experience with LLMs and computer vision; diffusion models and RL.
• Must have work authorization to work in Singapore.
• Intent to return to degree program after internship.
• Strong research publications at top ML/vision conferences.
• Experience developing diffusion models, flow matching, or multimodal RL.
• Experience with reward modeling or offline RL.
• Research in recommender systems, personalization, or sequential modeling.
• Contributions to open-source or reproducible research artifacts.

Responsibilities

• Collaborate with researchers and engineers to explore personalized retrieval, ranking, and recommendation list generation.
• Apply diffusion models and RL techniques to recommendation problems.

Skills

Python programming

PyTorch

LLMs

Computer vision

Diffusion models

Reinforcement learning

Research publications

Education

PhD in Computer Science / Electrical Engineering or related ML field

Tools

Python

PyTorch

Job description

We are seeking highly motivated Research Interns to advance the next generation of generative recommendation systems. Our research brings together large language models, computer vision, generative modeling, and reinforcement learning to understand content and user preferences and generate personalized recommendations.

As a Research Intern, you will collaborate with researchers and engineers to explore new approaches to personalized retrieval, ranking, and recommendation list generation. This opportunity is especially suited to researchers with backgrounds in LLMs and computer vision who want to apply diffusion models and reinforcement learning to recommendation problems.

• Has, or is in the process of obtaining, a Ph.D. in Computer Science, Electrical Engineering, or a related field, with a focus on machine learning, generative modeling, computer vision, or reinforcement learning
• Programming experience in Python and hands-on experience with deep learning frameworks such as PyTorch
• Experience with LLMs and computer vision, including familiarity with diffusion models and reinforcement learning
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
• Intent to return to the degree program after completing the internship
• Demonstrated research achievements through publications at leading conferences such as NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV, KDD, or RecSys
• Hands-on research experience developing diffusion models, flow matching methods, multimodal foundation models, or reinforcement learning algorithms
• Experience with reward modeling, offline reinforcement learning, preference optimization, or post-training of generative models
• Research experience in recommendation systems, personalization, or sequential modeling
• Contributions to open-source projects, benchmarks, or reproducible research artifacts
• Ability to identify research opportunities in practical applications, analyze experimental results, and independently drive research iterations while collaborating across teams