Research Scientist Intern, MSL - Agentic Post-Training (PhD)

Meta • Menlo Park, CA

7d • $7,650 - $12,667

About The Position

Meta is seeking Research Scientist Interns to join an agentic post-training team in Meta Superintelligence Labs (MSL). The team works on agentic post-training of Muse foundation models and end-to-end model post-training cycles, including supervised fine-tuning (SFT), reinforcement learning (RL), evaluation, and agentic task and environment creation. The work aims to improve agentic capabilities such as long-horizon task completion, complex tool use, search and deep research, and browser use. Interns can contribute core model improvements and apply research at scale to shape the user experience of Muse. Internships last 12 to 24 weeks, with various start dates throughout the year.

Requirements

• Currently has, or is in the process of obtaining, a Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a relevant technical field.
• Research experience in machine learning, deep learning, or natural language processing.
• Experience with deep learning frameworks such as PyTorch.
• Experience with Python, C++, C, Java, or other related programming 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

• Intent to return to the degree program after completing the internship or co-op.
• A track record of significant results, demonstrated through grants, fellowships, patents, first-authored publications at leading conferences, or previous internship projects.
• Research experience or publications in large language models, foundation models, post-training, agentic AI, or reasoning and coding.
• Experience with reinforcement learning and reward modeling.
• Experience with agentic systems, such as agent workflows, tool use, and RL environments, including core contributions to open-source libraries and frameworks for agentic LLMs or RL environments.
• Experience with LLM evaluation, including setting up machine learning experiments and analyzing results.
• Demonstrated engineering experience through an internship, work experience, coding competitions, or widely used contributions to open-source repositories such as GitHub.
• Experience working and communicating cross-functionally in a team environment.

Responsibilities

• Develop novel, state-of-the-art agentic post-training methods and supporting systems using deep learning and reinforcement learning techniques.
• Work across the full LLM post-training stack; execute, analyze, and iterate on large-scale post-training runs.
• Analyze and improve the efficiency, scalability, and stability of agentic training algorithms and deployed systems.
• Advance research and technology for intelligent agents that reason, reliably use tools, and complete long-horizon tasks.
• Collaborate with researchers and cross-functional partners, communicating research plans, progress, and results.
• Share research results through publications, presentations, open-source releases, or model contributions.
• When applicable, contribute research that can be applied to Meta product development.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: Ph.D. or professional degree