Research Scientist Intern, AI Core Machine Learning (PhD)
Description
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 a Research Intern to join Monetization Ranking & AI Foundations to answer the question: can an AI research agent get better at research by learning from its own experiments? This project explores recursive self-improvement for autoresearch: AI agents that form hypotheses, run experiments, analyze results, and then use that experience to improve how they conduct future research. Our team develops an agentic ML-research system that proposes, trains, and evaluates model changes at production scale, continually accumulating real research trajectories and outcomes from which the system can learn. The intern will help shape the research direction around open questions such as: Which parts of an agent should learn from experience—the LLM's weights through reinforcement-learning post-training, its harness of prompts, tools, memory, and orchestration, or both? How can we build reliable reward signals when real experiments are slow and expensive, while preventing reward gaming? Does self-improvement compound across rounds, or does it eventually plateau or collapse? Our interns closely collaborate with researchers and engineers within the organization and across the company (e.g., FAIR), push the boundaries of state-of-the-art research in AI and ML, and integrate innovations at unprecedented scale. The intern will have room to define their own research question within this space, with the goal of producing a full paper for ICLR, ICML, or NeurIPS. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.
Responsibilities
Conduct state-of-the-art research on recursive self-improvement, autoresearch, and self-improving AI agents. Design and evaluate agents that form hypotheses, use tools, run experiments, analyze outcomes, and learn from prior research trajectories. Develop methods to improve the underlying LLM through reinforcement-learning post-training and/or optimize the surrounding harness, including prompts, tools, memory, retrieval, and orchestration. Build reliable evaluation and reward signals for long-running, expensive ML experiments, including safeguards against reward hacking and regressions. Study whether gains compound across multiple self-improvement rounds and rigorously attribute improvements to model learning versus harness changes. Collaborate with researchers and engineers across varied disciplines, including communicating research plans, progress, and results. Publish research results at a top-tier machine learning conference and contribute research that can be applied to Meta product development.
Qualifications
Currently has or is in the process of obtaining a Ph.D. degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Computer Vision, Natural Language Processing, Reinforcement Learning, Optimization, Computational Statistics, Applied Mathematics, or related technical fields Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment Experience with deep learning frameworks such as Pytorch or Tensorflow Experience with Python, C++, C, Java, or other related languages Experience with research and building systems based on machine learning and/or deep learning methods Intent to return to degree program after the completion of the internship/co-op Research experience in one or more of the following areas: LLM agents, reinforcement learning for LLMs, automated ML research, AutoML, recommender systems, or evaluation of agentic systems Proven track record of achieving significant research results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, AAAI, KDD, IJCAI, CVPR, ICCV, ACL, NAACL, ICASSP, or similar Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub) Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources for large-scale training Experience working and communicating cross functionally in a team environment
Compensation: $7,650/month to $12,667/month + benefits