Meta is seeking 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.
Requirements
- 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
- 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
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.
Other
- Currently has, or is in the process of obtaining, a PhD degree in Computer Science, Machine Learning, Statistics, Operations Research, or related field
- 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
- Intent to return to degree program after the completion of the internship
