Job Description
Meta is looking for a PhD Research Intern to join the Adaptive Experimentation team, focusing on research and development of sample-efficient black-box optimization methods, including Bayesian optimization. The role involves collaboration with various teams to apply advanced machine learning techniques to real-world problems across Meta's platforms.
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
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
Benefits
- Employees at Meta are often offered comprehensive benefits, including medical/dental/vision coverage, mental-health resources, family planning support (fertility, adoption, surrogacy), and caregiving programs. Financial benefits typically include a competitive retirement plan, equity awards, life insurance, and access to financial coaching and legal support. Time-off benefits commonly feature generous PTO and holidays, unlimited sick time, various paid leaves, flexible “Global Travel Days,” and a 30-day paid break every five years.
