Research Scientist Intern, AI Systems and Infrastructure (PhD)
About The Position
Meta Fundamental AI Research (FAIR) is seeking PhD Research Scientist Interns to investigate and rethink the systems that power frontier AI research. The work addresses questions arising as large-scale AI training, inference, evaluation, and autonomous agents compete for accelerators, including how compute should be shared, scheduled, and operated at scale. Research spans hardware-software co-design, performance modeling, cluster scheduling, resource management, and infrastructure for agentic AI, with the aim of making AI research faster, more efficient, and more predictable at any scale. Interns join a multidisciplinary team of research scientists and engineers and pursue an open research question end to end, from framing the problem through experimentation and publication. Interns are expected to lead their own projects with mentorship rather than execute a predefined task list. Internships last 12 to 24 weeks, with start dates available throughout the year.
Requirements
- Currently have, or be in the process of obtaining, a PhD in computer science, computer engineering, electrical engineering, operations research, or a related field.
- Obtain work authorization in the country of employment by the time of hire and maintain ongoing work authorization during employment.
- Have a first-author publication at a top-tier systems or machine learning venue (OSDI, SOSP, NSDI, EuroSys, ASPLOS, SC, MLSys, or NeurIPS) or equivalent.
- Be proficient in Python and at least one systems language such as C++, Go, or Rust.
Nice To Haves
- Experience with cluster schedulers or resource managers such as Kubernetes or Slurm.
- Experience with workload characterization, performance modeling, or simulation of large-scale systems.
- Experience applying machine learning to systems problems, or building infrastructure for LLM training, inference, or agents.
- Intent to return to the degree program after completing the internship.
Responsibilities
- Identify and formulate open research problems in how AI workloads use and share large-scale compute.
- Plan and execute original research advancing AI systems and infrastructure, from new abstractions and algorithms to rigorous empirical evaluation.
- Collaborate with research scientists and engineers to translate findings into published work and open artifacts.
- Communicate results clearly to specialist and non-specialist audiences.
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
