Research Scientist Intern, Reinforcement Learning (PhD) 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.
Minimum Qualifications
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 collaborating within a 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
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
Preferred Qualifications
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
