AI Research Scientist, Multi-Agent Memory - FAIR, MSL

Meta•Menlo Park, CA

1d•$154,000 - $217,000

About The Position

Meta's Fundamental AI Research (FAIR) organization is seeking a Research Engineer to work on multi-agent systems and memory. The role focuses on driving fundamental breakthroughs that enable agents to reason, learn from experience, and collaborate on complex tasks. It contributes across research, engineering, and deployment for product and frontier model efforts.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
PhD degree in Computer Science, Mathematics, or a similar discipline.
Proven track record of publications at top-tier AI and NLP conferences, such as NeurIPS, ICML, ICLR, ACL, NAACL, or EMNLP.
Demonstrated experience training and fine-tuning language models and Transformers, and conducting experimentation beyond black-box usage.
Must be able to obtain work authorization in the country of employment.

Nice To Haves

Experience working with deep learning frameworks, building research prototypes, and communicating cross-functionally in a team environment.
Research experience in long-term memory, multi-agent systems, agent orchestration, self-improvement, or automated scientific research.
Experience with large language model pre-training, fine-tuning, long-context modeling, context compression, or evaluation pipelines at scale.
Experience building, evaluating, and designing controlled experiments for language-model agents, including tool use, memory mechanisms, or collaborative workflows.
Proven track record of achieving significant research results, as demonstrated by publications at leading AI and NLP conferences.

Responsibilities

Design and execute large-scale research experiments advancing machine intelligence, language models, multi-agent communication, test-time scaling, and recursive self-improvement.
Steer projects from early exploration through evaluation on benchmark datasets such as SWE-bench, RSI-Exam, and PaperBench.
Contribute to experimental design, harness engineering, maintenance of large codebases, evaluations, and end-to-end performance tracking.
Publish novel algorithmic techniques at top-tier venues, foster open joint experimentation, and mentor team members.
Lead cross-functional research initiatives and establish solution recipes that bridge research clusters to production serving environments.