About this role
Meta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.
Meta is seeking Research Interns to join our AI Search research team. We are committed to advancing the field of artificial intelligence by making fundamental advances in how AI agents learn from their own experience — improving their search and reasoning workflows, retaining useful knowledge, and transferring those gains to unfamiliar tasks. A central research challenge is making repeated self-improvement reliable when feedback is sparse or noisy.
The intern will work in one of three directions, scoped closer to the start date together with the intern manager:
-Learning to improve research workflows — agents learn better search, tool-use and verification strategies from prior attempts by improving their harness: the workflow, memory, tools and feedback loop around the model. Topics include meta-learning across tasks, automated agent design, and learning from natural-language feedback.
-Persistent memory and continual learning — agents retain useful lessons and update knowledge across tasks, whether in context, in compressed memory, or in weights, without forgetting what they already know. Topics include long-context modeling, context compression, continual learning, and test-time training.
-Learning from imperfect feedback — turning noisy judgments and failed attempts into useful learning signals, and improving the strategy an agent uses to learn, including how it selects and evaluates its next improvement. Topics include reward learning, credit assignment, and learning to learn.
Across all three directions, the key test is 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. Recent internships on this team have produced work such as REFRAG, a new architecture for retrieval-augmented generation.
Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.
Responsibilities
Develop novel state-of-the-art methods for self-improving, agentic AI systems, leveraging a range of deep learning and reinforcement learning techniques.
Design and run experiments that test whether agent improvements generalize and transfer to unseen tasks.
Perform research to advance the science and technology of intelligent machines.
Help analyze and improve the reliability and robustness of the resulting methods.
Collaborate with researchers and cross-functional partners, including communicating research plans, progress, and results.
Disseminate and publish research results, and contribute to research that can be applied to Meta product development.
Qualifications
Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a relevant technical field
Must 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 Intent to return to the degree program after the completion of the internship/co-op
Proven track record of achieving significant results as demonstrated by 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: agent workflows, tool use, agentic search, or retrieval-augmented generation (RAG)
Experience with reinforcement learning, reward modeling, or credit assignment
Experience with continual/lifelong learning, meta-learning, or test-time training
Experience with long-context modeling, memory, context compression, or efficient inference
Experience with LLM evaluation and setting up ML experiments and analyzing their 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 via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)