Meta is seeking a PhD Research Intern to join the Adaptive Experimentation team, within our Central Applied Science Org. The mission of the team is to do cutting-edge research and build new tools for sample-efficient black-box optimization (including Bayesian optimization) that democratize new and emerging uses of AI technologies across Meta, including Facebook, Instagram, and AR/VR. Applications range from AutoML and optimizing Generative AI models to automating A/B tests, contextual decision-making, and black-box optimization for hardware design.PhD Research Interns will be expected to work closely with other members of the team to conduct applied research at the intersection of Probabilistic ML, Bayesian optimization, AutoML, and Deep Learning, while working collaboratively with teams across the company to solve important problems.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, Adaptive Experimentation (PhD) Responsibilities

Develop and apply new methods and modeling approaches for adaptive experimentation methods, such as Bayesian optimization and active learning to new and emerging applications at Meta.

Synthesize and apply insights from the relevant academic literatures to Meta’s products and infrastructure.

Work both independently and collaboratively with other scientists and engineers within and outside the team.

Apply excellent communication skills to engage diverse audiences on technical topics.

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Minimum Qualifications

Currently has, or is in the process of obtaining, a PhD degree in Computer Science, Machine Learning, Statistics, Operations Research, or related field

Research experience with Bayesian optimization, probabilistic modeling, amortized inference, sample-efficient decision-making, or similar topics

Experience with developing in Python and PyTorch

Expertise in empirical research, including manipulating and analyzing complex data and communicating quantitative analyses

Experience working and communicating cross-functionally in a team environment

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

Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as JMLR, NeurIPS, ICML, ICLR, AISTATS, UAI, KDD, etc

Knowledge for disseminating new methods through open-source projects and/or academic publications

Experience with transformers,diffusion model architectures, and conducting research with and evaluating LLMs

Research experience with Preference learning approaches, causal inference, and applied statistics

Intent to return to degree program after the completion of the internship

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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