We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, computer vision, audio and speech processing, natural language processing, machine learning, reinforcement learning, computational statistics, and applied mathematics. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Reinforcement Learning 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.
• Publish research results and contribute to research that can be applied to Meta product development.

• Currently has or is in the process of obtaining a Master 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.

• Interest in pursuing a PhD in ML/AI.
• Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches.
• ML/ AI research and/ or work experience in deep reinforcement learning. Possibly evidence of research achievements in terms of publications at leading workshops or conferences such as NeurIPS, ICLR, ICML, CoRL, 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.