Meta is building technologies to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization. Meta is seeking Research Scientists to join Fundamental AI Research (FAIR). 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 solving systems challenges in areas such as deep learning, computer vision, audio and speech processing, natural language processing. Our researchers have opportunities to make core algorithmic advances and apply their ideas at an unprecedented scale. The mission of Meta FAIR's SysML research is to explore and advance systems to unlock the potential of AI technologies. We aim to sustainably accelerate machine learning innovations with novel system solutions and advance AI infrastructures at scale.Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta. Currently has or is in the process of obtaining a Ph.D. degree in Computer Science or Computer Engineering with a focus in Systems and Machine Learning or relevant technical fields. Degree must be completed prior to joining Meta. Experience with Python, C++, C, Lua or other related languages and with PyTorch framework. Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment. 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, ICML, ICLR, MLSys, ISCA, ASPLOS, CGO, PLDI, PACT, HPCA, MICRO, or similar. Experience developing and optimizing systems for at-scale machine learning execution. Experience in real-system implementations. Experience devising data-driven models and real-system experiments and design implementation for AI system optimization. Experience with scalable machine learning systems, resource-efficient AI data and algorithm scaling, or neural network architectures. Experience with memory and energy-efficient AI systems, environmentally-sustainable AI system designs, or AI-driven system optimization. Experience solving analytical problems using quantitative approaches. Experience in utilizing theoretical and empirical research to solve problems. Experience working and communicating cross functionally in a team environment.