ML Innovation for Products, PhD Research Intern

The ML Innovation for Products (MLIP) team is an applied machine learning and research team within Meta's Ranking & Foundational AI org, focused on the state-of-the-art (SOTA) 0-to-1 ML that powers retrieval, ranking, and auction across Meta's ads systems. This is a very unique team that functions like an applied research lab with a production deploy button: we imagine, evaluate, prototype, and ship 10x ideas into one of the largest recommender systems in the world, a system that serves billion-scale users every single day and is the engine behind Meta's revenue.

Responsibilities

• Conduct state-of-the-art research to advance the science and technology of Machine Learning and Artificial Intelligence.
• Develop novel algorithms and corresponding systems, leveraging various AI and ML techniques including LLMs and agentic pipelines.
• Analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.
• Collaborate with researchers and engineers across varied disciplines, including communicating research plans, progress, and results.
• Publish research results and contribute to research that can be applied to Meta product development.

Minimum 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.

Preferred Qualifications

• Intent to return to degree program after the completion of the internship/co-op.
• 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.
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories.
• 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.