About the position
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.
Responsibilities
- Make core algorithmic advances in AI technologies.
- Apply research ideas at an unprecedented scale.
- Explore and advance systems to unlock the potential of AI technologies.
- Develop and optimize systems for at-scale machine learning execution.
- Conduct real-system implementations and experiments for AI system optimization.
- Utilize theoretical and empirical research to solve problems.
Requirements
- Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, or relevant technical field.
- 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.
- 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, and first-authored publications at leading workshops or conferences.
- 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 for AI system optimization.
- Experience with scalable machine learning systems and resource-efficient AI data and algorithm scaling.
- Experience with memory and energy-efficient AI systems.
Nice-to-haves
- Experience solving analytical problems using quantitative approaches.
- Experience working and communicating cross functionally in a team environment.
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