Research Scientist Intern, PhD — Meta Superintelligence Labs

Meta•Menlo Park, CA

1d•$7,650 - $12,667

About The Position

Meta is seeking a PhD research intern to join its AI Search research team within Meta Superintelligence Labs. The team advances artificial intelligence research on agents that learn from their own experience, improve search and reasoning workflows, retain useful knowledge, and transfer learning to unfamiliar tasks. A central challenge is making repeated self-improvement reliable when feedback is sparse or noisy. The intern will work on one of three research directions, scoped closer to the start date with the intern manager: improving agent research workflows through better search, tool use, verification, memory, and feedback; persistent memory and continual learning that preserve useful lessons across tasks without forgetting; or learning from imperfect feedback, including reward learning, credit assignment, and selecting and evaluating improvements. Across all directions, research will test whether improvements transfer to new tasks. The goal is to develop novel methods and experiments toward a first-author submission at a leading ML/NLP venue. The internship lasts 12 to 24 weeks, with various start dates throughout the year.

Requirements

Currently have or be in the process of obtaining a Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a relevant technical field.
Obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
Research experience in machine learning, deep learning, and/or natural language processing.
Experience with Python.
Experience with deep learning frameworks such as PyTorch or TensorFlow.

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, or first-authored publications at leading workshops or conferences such as NeurIPS, ICLR, ICML, ACL, EMNLP, or COLM.
Research experience in large language models, foundation models, or post-training.
Experience with agentic systems, including agent workflows, tool use, agentic search, or retrieval-augmented generation (RAG).
Experience with reinforcement learning, reward modeling, or credit assignment.
Experience with continual or lifelong learning, meta-learning, or test-time training.
Experience with long-context modeling, memory, context compression, or efficient inference.
Experience with LLM evaluation, setting up machine learning experiments, and analyzing results.
Experience with Hugging Face Transformers and training or fine-tuning transformer models.
Experience with distributed training.
Experience working and communicating cross-functionally in a team environment.
Demonstrated software engineering experience through an internship, work experience, coding competitions, or widely used open-source contributions.

Responsibilities

Develop novel state-of-the-art methods for self-improving agentic AI systems using deep learning and reinforcement learning techniques.
Design and run experiments to test whether agent improvements generalize and transfer to unseen tasks.
Conduct research to advance the science and technology of intelligent machines.
Analyze and improve the reliability and robustness of resulting methods.
Collaborate with researchers and cross-functional partners, communicating research plans, progress, and results.
Disseminate and publish research results, and contribute to research that can be applied to Meta product development.