Research Scientist Intern, FAIR – Multi-Agent Systems and Memory (PhD)
About The Position
Meta is seeking Research Interns to join its Fundamental AI Research (FAIR) Communication & Language teams, working on multi-agent systems and memory. The teams pursue fundamental AI advances that enable agents to reason, learn from experience, and collaborate on complex tasks. The internship focuses on how AI agents can retain and use knowledge across extended interactions, share information effectively, and adapt workflows based on experience, connecting memory and multi-agent collaboration with self-improving systems for automated scientific research. Potential research directions include compact memory representations, improved inter-agent communication and coordination, and automated optimization of agent harnesses. The specific research question will be shaped by the intern’s interests and early experimental findings. Internships last 12 to 24 weeks, with various start dates throughout the year.
Requirements
- Currently has or is in the process of obtaining a PhD in Computer Science, Machine Learning, Artificial Intelligence, Natural Language Processing, or a related field.
- Research and/or work experience in large language models, machine learning, deep learning, reinforcement learning, or related areas.
- Experience in Python, C++, or other related languages.
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
Nice To Haves
- A track record of significant research results, demonstrated by grants, fellowships, patents, or publications at leading AI conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, COLM, AAAI, or IJCAI.
- Research experience in agent memory, context compression, multi-agent systems, agent orchestration, self-improvement, or automated scientific research.
- Experience building and evaluating language-model agents, including tool use, memory mechanisms, or collaborative workflows.
- Experience with deep learning frameworks such as PyTorch and implementing research prototypes.
- Contributions to open-source libraries and frameworks are welcome.
- Experience designing controlled experiments, analyzing agent trajectories or large-scale experimental results, and using theoretical and empirical research to solve problems.
- Experience working and communicating cross-functionally in a team environment.
- Intent to return to a degree program after completion of the internship or co-op.
Responsibilities
- Conduct fundamental research on memory and multi-agent collaboration to improve AI agents on complex, long-running tasks.
- Brainstorm with research mentors, review relevant literature and existing systems, and formulate focused research questions.
- Develop novel algorithms and architectures for retaining and retrieving context, sharing knowledge across agents, or adapting agent workflows based on experience.
- Implement prototypes and conduct rigorous experiments on automated research and related agentic benchmarks, comparing against strong baselines.
- Analyze agent trajectories and failure modes to assess memory utilization, coordination, task performance, computational cost, and generalization.
- Draft research reports and/or publications and present research outcomes to internal and/or external audiences.
- Contribute research that can be applied to Meta product development.
Benefits
- Benefits are offered; details are not specified.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
