Research Scientist Intern, Generative Recommendation (PhD)

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.

Research Scientist Intern, Generative Recommendation (PhD) Responsibilities:

• Conduct research on generative recommendation, including autoregressive transformers, diffusion models, and flow matching for personalized retrieval and ranking.
• Explore multimodal modeling approaches that combine images, video, text, and user interaction histories to learn content representations and user preferences.
• Develop reinforcement learning, reward modeling, and preference optimization methods to improve recommendation quality and long-term user satisfaction.
• Design, implement, and evaluate novel model architectures and training algorithms through rigorous experimentation.
• Collaborate closely with researchers and engineers to translate research ideas into scalable recommendation methods.
• Contribute to publications at leading AI and machine learning conferences, and present findings that help shape ongoing research and future directions.
  
  

Minimum Qualifications:

• 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
  
  

Preferred Qualifications:

• 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
  
  

About Meta:

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.

Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.