Research Scientist Intern, Reinforcement Learning , (PhD) (France)

Meta is seeking Research Interns to join FAIR, our world-class research lab. We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. In particular, 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, behavioral foundation models, exploration-exploitation and responsible RL. The candidate should have a solid experience in machine learning and reinforcement learning, as testified by original and impactful publications in this domain at top tier conferences in machine learning (NeurIPS, ICML, ICLR). We are proposing Summer, Fall, and Winter start dates. Internships will be awarded on a rolling basis and candidates are encouraged to apply early. Applicants are expected to be pursuing a PhD in reinforcement learning; outstanding Masters and Bachelor students may also be considered. To learn more about our research visit https://ai.facebook.com.

Research Scientist Intern, Reinforcement Learning , (PhD) (France) Responsibilities:

• Collaborate on research to advance reinforcement learning agents to learn general models that can be used to solve a wide range of problems with little to no retraining
• Devise, implement, and test new algorithms for un-/self-supervised RL
• Influence progress of relevant research communities by producing publications
• Collaborate and increase productivity on existing FAIR projects as a contributing team member

Minimum Qualifications:

• Currently has, or is in the process of obtaining, a Masters/PhD degree in Computer Science or related fields.
• Solid background on the foundations of reinforcement learning
• Ability to implement and run reinforcement learning algorithms in complex environments
• Experience collaborating within a team to solve complex problems
• Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
• Un-/self-supervised reinforcement learning
• Representation learning in RL
• Exploration-exploitation in RL

Preferred Qualifications:

• Publications or experience in reinforcement learning, machine learning, optimization, computer science, statistics, applied mathematics, or data science
• Experience solving analytical problems using quantitative approaches
• Ability to manipulate and analyze complex, large scale, high-dimensionality data from varying sources
• Ability to communicate complex research in a clear, precise, and actionable manner
• Knowledge in deep learning frameworks (such as pytorch, tensorflow), C, C++, Python
• Experience building systems based on machine learning and/or deep learning methods
• Intent to return to degree-program after the completion of the internship/co-op

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