Central Applied Science (CAS) is a research and development team, working to improve Meta’s products, infrastructure, and processes. We generate real-world impact through a combination of scientific rigor and methodological innovation. Our focus is on longer-term, foundational work that addresses new opportunities and challenges across the Meta family of apps. The work we do enhances the Meta family of apps that enable billions of people to communicate with each other daily.Central Applied Science is interdisciplinary, with expertise in computer science, statistics, machine learning, economics, political science, operations research, marketing science and sociology, among many other fields. This diversity of perspectives enriches our research and expands the scope and scale of projects we can address. We deliver value through collaborative projects with other groups at Meta and with the academic community. In addition, we build and open-source technical products aligned with our areas of expertise.The Graph Science and Statistics team within Central Applied Science is looking for PhD research interns with expertise in statistical modeling, Graph Learning and GenAI Models and Evals. The team works closely with various product groups throughout Meta, bringing expertise in statistical methodology for model evaluation and bias correction, graph and representation learning, and GenAI model development and application for various applications. We solve critical problems that enable Meta to make robust and trustworthy strategic decisions and build products that best serve and protect their users.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

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Research Scientist Intern, Graph Science and Statistics Research (PhD) Responsibilities

Build pragmatic, scalable, and statistically rigorous solutions to mission critical inferential and decision problems by leveraging or developing state of the art statistical and machine learning methodologies on top of Meta’s unparalleled data infrastructure.

Apply excellent communication skills in order to develop cross-functional partnerships throughout the company and spread statistical best practices.

Be able to work both independently and collaboratively with other scientists, engineers, designers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value to Meta’s community of over 3.5 billion users.

Think creatively, proactively, and futuristically to identify new opportunities within Meta’s long term roadmap for data-scientific contributions.

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Minimum Qualifications

Currently has, or is in the process of obtaining, a PhD degree in a quantitative field such as Statistics, Computer Science, Information Science, Data Science, Quantitative Marketing or relevant technical field

Experience in machine learning and statistical research covering at least one of the following domains: graph and sequence modeling, statistical modeling, network science, marketing science, relational learning, user modeling, model evaluation or generative AI

1+ years in programming, analysis, and visualization using computing software and libraries such as Python or R

Interpersonal experience: cross-group and cross-culture collaboration

Experience in scalable dataset assembly/data wrangling, such as Hive, Spark or Presto

Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

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Preferred Qualifications

Intent to return to degree-program after the completion of the internship/co-op

Proven track record of achieving significant results as demonstrated by previous internships or industry experience, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as KDD, The Web, AI STATS, AAAI, NeurIPS, ICML, or similar

Experience working and communicating cross functionally in a team environment

For those who live in or expect to work from California if hired for this position, please click here for additional information.

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