Research Scientist Intern, Adaptive Experimentation (PhD)

Meta Platforms, Inc.•Menlo Park, CA

42d

About The Position

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.

Requirements

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

Nice To Haves

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

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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