About the Role

This is a PhD-level research scientist internship focused on reinforcement learning at Meta. The intern will develop novel RL algorithms, analyze their efficiency, and collaborate with researchers to advance AI technology. The role involves applying research to product development and requires expertise in deep learning frameworks and sequential decision-making.

Key Responsibilities

• Develop novel state-of-the-art reinforcement learning algorithms and corresponding systems, leveraging various deep learning techniques.
• Analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.
• Perform state of the art research to advance the science and technology of Machine Learning and Artificial Intelligence.
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Contribute to research that can be applied to Meta product development.

Required Skills & Qualifications

Must Have:

• Currently has or is in the process of obtaining a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, Reinforcement Learning, Mathematics, or relevant technical field.
• Solid background on the foundations of reinforcement learning.
• Ability to implement and run reinforcement learning algorithms in complex environments.
• Experience with Python, C++, C, Java or other related languages.
• Experience with deep learning frameworks such as Pytorch or JAX.
• Experience building systems based on machine learning and/or deep learning methods.
• Research experience with algorithms for sequential decision-making, e.g., planning, reinforcement learning, or similar.
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

Nice to Have:

• Intent to return to a degree program after the completion of the internship/co-op.
• 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, ICML, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ICASSP, or similar.
• Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches.
• ML/ AI research and/ or work experience in deep reinforcement learning.
• 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.