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.

• Develop and apply new adaptive experimentation methods, such as Bayesian optimization, Active learning, and Deep 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.

• 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 Machine Learning, Sample-Efficient Decision-Making, or similar topics.
• Experience with developing and debugging in Python with PyTorch or related Deep Learning frameworks.
• Expertise in empirical research, including manipulating and analyzing complex data and communicating quantitative analyses.
• Interpersonal experience: cross-group and cross-culture collaboration.
• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment.

• 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, AISTATS, UAI, KDD, etc.
• Knowledge for disseminating new methods through open-source projects and/or academic publications.
• Experience working and communicating cross-functionally in a team environment.
• Experience with generative AI approaches, including transformer and diffusion model architectures.
• Research experience in causal inference and applied statistics.
• Intent to return to degree-program after the completion of the internship/co-op.