Research Scientist Intern, User Modeling and Reinforcement Learning (PhD)

We are advancing representation learning to better understand how people's interests and intent evolve over time. You will help bring new research ideas into production systems at Meta's scale, shaping more relevant experiences and creating lasting value for people and businesses.

As an intern, you will own a focused research project with close mentorship from researchers and engineers. The work connects new ideas to rigorous experiments and applications in Meta's ads systems, with opportunities to contribute to scientific publications and product development.

Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long, with various start dates throughout the year.

Responsibilities

Conduct original research to advance artificial intelligence and machine learning. Explore novel ideas and help shape future research directions. Collaborate with researchers and engineers across Meta on ambitious research challenges. Bring promising research ideas to life in products used by people and businesses around the world. Share research findings through publications and engagement with the broader research community.

Qualifications

Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, Reinforcement Learning, Statistics, Applied Mathematics, or a related technical field 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++, or another relevant programming language Experience conducting research and building systems based on machine learning or deep learning methods Intent to return to a degree program after completion of the internship/co-op Experience in one or more of self-supervised learning, transformers, temporal sequence modeling, representation learning, or recommender systems Experience in reinforcement learning, causal inference, counterfactual evaluation, or learning from delayed and censored outcomes 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, KDD, RecSys, and similar conferences or workshops Experience manipulating and analyzing complex, high-volume, high-dimensional data and designing controlled machine learning experiments Demonstrated software engineering experience through internships, research implementations, or open-source contributions Self-directed quantitative problem-solving and experience working and communicating across disciplines in a team environment