Postdoctoral Researcher, Reinforcement Learning (PhD) – Paris

Meta is seeking a Postdoctoral Researcher to join a reinforcement learning team at FAIR, our world-class research lab. We are seeking individuals to work with our reinforcement learning team to build novel algorithms grounded in solid principles that can scale to efficiently solve complex real-world problems. Our research spans different aspects of reinforcement learning with particular focus on unsup/self-supervised RL to build behavioral foundation models. The candidate should have a solid background in machine learning and reinforcement learning, with experience in building and managing complex reinforcement learning pipelines at scale.

Postdoctoral Researcher, Reinforcement Learning (PhD) – Paris Responsibilities:

• Collaborate with the research supervisor and the team to devise novel behavioral foundation models that enable agents to solve a wide range of problems with little to no retraining.
• Design and implement algorithms, train state of the art models on large data, and evaluate their performance.
• Share research advancements through publications, libraries, models, or demos.

Minimum Qualifications:

• Currently has or is in the process of obtaining a PhD degree or completing a postdoctoral assignment in the field of Computer Science or similar. Degree must be completed prior to joining Meta.
• Effective programming skills.
• Solid background on the foundations of reinforcement learning.
• Experience in working on complex machine learning code bases at scale.
• Experience collaborating within a research team to solve complex problems.
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.

Preferred Qualifications:

• First-authored publications at peer-reviewed conferences (ICML, ICLR, NeurIPS, CoRL, ICRA, AAAI, AAMAS or similar)
• Demonstrated machine learning experience in one of the following: internship, open-source activity, data science competitions
• Experience in creating high-performance implementations in deep learning frameworks (such as pytorch, tensorflow), C, C++, Python Experience working and communicating cross functionally in a team environment.
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward.
• Experience working and communicating cross functionally in a team environment.

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