Applied AI Research Scientist - Reinforcement Learning

The Applied Reinforcement Learning team is responsible for all reinforcement learning applications across Meta's Family of Apps and Reality Labs. We conduct state-of-the-art industry research in reinforcement learning to bridge the gap between theoretical reinforcement learning and real-world problems. We also own Meta's central reinforcement learning platform, which is also open-sourced as the production ready reinforcement learning platform.

We are seeking Research Scientists to help us with a vision to bring reinforcement learning to large scale real-world systems. Our research scientists have an opportunity to work with top researchers in applied reinforcement learning to conduct industry research on reinforcement learning algorithms with a goal to achieve a better algorithm, design an algorithm in real-world applications, and apply their ideas at an unprecedented scale.Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Has obtained a Ph.D. degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Reinforcement Learning, Mathematics, or relevant technical field. Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment. Experience with Python, C++, Java or other related language. Experience with deep learning frameworks such as Pytorch or Tensorflow. Experience with reinforcement learning in applied areas beyond simple simulators. Demonstrated experience in solving analytical problems using quantitative approaches. Experience manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources. Experience building systems based on machine learning, reinforcement learning and/or deep learning methods. 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 NeurIPS, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, or similar. Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub). Experience working and communicating cross functionally in a team environment.