Research Intern, AI Systems and Machine Learning (PhD)

Summary: Meta is seeking Research Interns to join Monetization Ranking & AI Foundations to advance the science and technology of Machine Learning Systems and Infrastructures. The role involves conducting state-of-the-art research, collaborating with researchers and engineers, and publishing findings that can be applied to Meta product development. Responsibilities: - Conduct state-of-the-art research to advance the science and technology of Machine Learning Systems and Infrastructures in various technology areas, e.g., ranking and retrieval systems, distributed training/inference optimizations - Contribute to research that leads to innovations in scalable machine learning systems, resource-efficient AI and neural network architectures for data and algorithm scaling, memory/communication and energy-efficient AI systems, and hardware/software co-design, AI-driven compiler, system design and performance optimization, etc - Analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms - Collaborate with researchers and engineers across varied disciplines, including communicating research plans, progress, and results - Publish research results and contribute to research that can be applied to Meta product development Required Qualifications: - Currently has or is in the process of obtaining a Ph.D. degree in Machine Learning, Systems, Artificial Intelligence, Computer Science, or related fields - 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, or other related languages - Experience in real-system implementations (e.g., GPU, NPU) - Experience building systems based on machine learning and/or deep learning methods Preferred Qualifications: - Intent to return to the degree program after the completion of the internship/co-op - Proven track record of achieving significant research results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, MLSys, ISCA, ASPLOS, CGO, PLDI, PACT, HPCA, MICRO, or similar - Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub) - Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches - Experience working and communicating cross functionally in a team environment Required Skills: Machine Learning, Python, C++, Deep Learning Important Skills: Real-system implementations, Research publication Nice-to-Have Skills: Analytical problem solving, Team collaboration Benefits: Benefits