- 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
- 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
- Research or software engineer experience demonstrated via grants, fellowships, patents, internship, work experience, and/or coding competitions.
- Experience with performance optimization of machine learning models for on-device inference
- Submitted first-authored publications at peer-reviewed conferences (e.g. tinyML, NeurIPS, MLSys, ICML, or similar).
- Intent to return to degree-program after the completion of the internship/co-op
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