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.

• Perform research to advance the science and technology of machine learning and reinforcement learning. Devise better data-driven models of human behavior.
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Apply state of the art reinforcement learning technologies to achieve production impact and improve Meta users' experience.

• 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.