Research Scientist Intern, PyTorch On-Device (PhD)

Job Description:

PyTorch is Meta's deep learning framework for fast, flexible AI/ML experimentation used across industry and backing all of Meta's ML workloads. More details at https://pytorch.org/ Our team brings PyTorch to edge devices through the use of compilers, an optimized runtime, and leveraging unique mobile hardware (CPU, GPU, NPU, DSP) for inference and training. Team scope: - Develop ExecuTorch as the PyTorch solution for on-device AI - Partner with Reality Labs to run AI on AR/VR hardware - Partner with Meta family of apps for running AI on iOS and Android - Partner with hardware vendors on high-performance AI kernels - Provide on-device AI inference, feature stores, benchmarking, and model delivery Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.
Develop new or apply existing performance techniques to on-device AI. Explore quantization, sparsity, and model/software co-design as solutions. Apply knowledge and research to advance the state-of-the-art in on-device machine learning frameworks. Collaborate with users and developers of PyTorch and ExecuTorch to enable new use cases inside and outside Meta

Candidate Requirements:

Currently has, or is in the process of obtaining, PhD degree in the field of Computer Science or a related STEM field Experience in ML compilers, sparsity, quantization, kernel development, or similar as applied to on-device and highly-constrained environments Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Experience working on other AI/ML optimized runtime stacks Experience with performance optimization of machine learning models for on-device inference Intent to return to degree program after the completion of the internship/co-op 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, MLSys, ASPLOS, PLDI, CGO, PACT, ICML, or similar Experience working and communicating cross functionally in a team environment