Applied AI Scientist, Adaptive Experimentation
Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started.
Facebook is seeking AI Scientists to join the Adaptive Experimentation team. The mission of the team is to do cutting-edge research and build new tools for reinforcement learning and black-box optimization that democratize new and emerging uses of AI technologies across Facebook, Instagram, and sister companies. Applications range from AutoML, to automating A/B tests, to contextual decision-making for mobile and server-side infrastructure, to black-box optimization for hardware design.
Scientists will be expected to conduct cutting-edge applied research in the area of reinforcement learning and black-box optimization while working collaboratively with teams across the company to solve important problems. AI Scientists are expected to work alongside with applied statisticians specializing in causal inference and experimental design, so additional experience in these areas is a plus.
- Develop and apply new adaptive experimentation methods, such as multi-armed bandit optimization and reinforcement learning, to new and emerging applications.
- Ph.D. in computer science, operations research, statistics, or related field with 2+ years of research experience with reinforcement learning or neural networks
- 2+ years of research experience and a publication track record in at least one of the following:
- Deep reinforcement learning
- Contextual bandits
- Evolutionary strategies or evolutionary algorithms
- Bayesian optimization
- Experience with developing and debugging in with Python with PyTorch, TensorFlow, Caffe, or related frameworks
- A passion for disseminating new methods through open-source projects and/or academic publications.
- First-author publications at peer-reviewed AI conferences (e.g. NIPS, ICML, ICLR, AISTATS, UAI).
- Experience with causal inference, applied statistics, or A/B testing.
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