Research Scientist Intern, FAIR – Multi-Agent Systems and Memory (PhD)

Meta; Bellevue, Seattle, New York (WA, WA, NY); On-site; USD 34,000 - 55,000; Part time

Job summary

Meta is seeking Research Interns to join our FAIR Communication & Language teams in Bellevue, WA for 12–24 weeks. You will work on multi-agent systems, memory and language models, and fundamental AI breakthroughs.

We welcome PhD candidates with experience in Python, PyTorch, RL, and large language models, and a track record of research results. You will prototype ideas at scale and contribute to core algorithms.

Qualifications

• Pursuing a PhD in CS/ML/AI/NLP with strong research
• Experience in large language models, ML, RL
• Experience with Python, C++, or related languages
• Authorization to work in the United States
• Proven research results (grants, publications) at leading AI conferences
• Research experience in agent memory, context compression, multi-agent systems
• Experience building language-model agents with tools, memory, or collaboration
• Experience with PyTorch and prototyping research code
• Contributions to open source libraries are welcome
• Ability to design controlled experiments and analyze trajectories

Responsibilities

• Investigate how AI agents retain knowledge across extended interactions
• Develop compact memory representations and retrieval strategies
• Improve inter-agent communication and coordination mechanics
• Explore automatic optimization of agent harnesses and workflows
• Evaluate ideas at scale and communicate findings

Skills

Multi-agent systems

Agent memory

Long-context modeling

Self-improving agents

Reinforcement learning

Python

C++

Research experience

Education

PhD in CS/ML/AI/NLP

Tools

PyTorch

Open source contributions

Job description

Meta is seeking Research Interns to join our Fundamental AI Research (FAIR) Communication & Language teams working on multi-agent systems and memory. These teams are committed to advancing artificial intelligence through fundamental breakthroughs that enable agents to reason, learn from experience, and collaborate on complex tasks. We are looking for individuals passionate about large language models, agentic systems, machine learning, and reinforcement learning. Our interns have the opportunity to make core algorithmic advances and evaluate their ideas at scale.

This internship will investigate how AI agents can retain and use knowledge across extended interactions, share information effectively, and adapt their workflows based on experience. Our research connects memory and multi-agent collaboration with self-improving systems for automated scientific research. Potential directions include developing compact memory representations, improving inter-agent communication and coordination, and automatically optimizing agent harnesses. The specific research question will be shaped by the intern's interests and early experimental findings.

We are especially interested in candidates with expertise in one or more of the following areas:

• Multi-agent systems, collaboration, coordination, communication, and shared knowledge
• Agentic memory, long-term memory, implicit or parametric memory, and memory retrieval and utilization
• Long-context modeling, context compression and compaction, and KV-cache compression
• Self-improving agents, adaptive agent workflows, agent harness optimization, and test-time learning and adaptation
• Reasoning, planning, tool use, reinforcement learning, and automated scientific research

Our team at Meta AI offers twelve (12) to twenty-four (24) weeks long internships and we have various start dates throughout the year. To learn more about our research, visit https://ai.meta.com/research/.

• Currently has or is in the process of obtaining a PhD degree 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
• Proven track record of achieving significant research results, as 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 degree-program after the completion of the internship/co-op