Postdoctoral Researcher, Central Applied Science, Adaptive Experimentation (PhD)
Meta

Job Info

Meta is seeking a visiting postdoctoral researcher to join the Adaptive Experimentation team. The mission of the team is to do cutting-edge research and build new tools for sample-efficient learning and decision making (including Bayesian optimization) that democratize new and emerging uses of AI technologies across Meta, including Facebook, Instagram, and Reality Labs. Applications range from AutoML and optimizing Generative AI models, to automating A/B tests, to contextual decision-making to black-box optimization for hardware design.Visiting Researchers will be expected to conduct cutting-edge applied research at the intersection of Bayesian optimization, AutoML, and Deep Learning, while working collaboratively with teams across the company to solve important problems.

Postdoctoral Researcher, Central Applied Science, Adaptive Experimentation (PhD) Responsibilities:

• Develop algorithms and models for adaptive experimentation.
• Conduct research to advance the state of the art in Bayesian optimization, active learning, and/or Bayesian modeling.
• Contribute to and apply research that advances Meta Platforms products.
• Conduct and collaborate on research projects within a distributed team.

Minimum Qualifications:

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
• Currently has or is in the process of obtaining a PhD in the field of computer science, machine learning, statistics, operations research, a related field, or equivalent practical experience. Degree must be completed prior to joining Meta.
• Experience with developing and debugging in Python with PyTorch or related deep learning frameworks.
• Research experience and a publication track record in at least one of the following: Bayesian optimization, active learning, probabilistic methods and/or deep learning.
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

Preferred Qualifications:

• Experience with Generative AI.
• Experience with causal inference and/or applied statistics.
• Experience disseminating new methods through open-source projects.
• Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward.
• Experience working and communicating cross functionally in a team environment.
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences such as NeurIPS, ICML, ICLR, JMLR, AAAI, UAI, KDD, AISTATS or similar.

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