- 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 diverse disciplines, 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 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.
- 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.
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