The PyTorch Compiler team is dedicated to making PyTorch run faster and more resource-efficient without sacrificing its flexibility and ease of use. The team is the driving force behind PT2, a step function change in PyTorch’s history that brought compiler technologies to the core of PyTorch.
PT2 technologies have gained industry-wide recognition since their first release in March 2023. The team is committed to building the PT2 compiler that withstands the test of time while striving to become the #1 ML framework compiler in the industry.
The team is highly innovative, passionate about the technologies we build, and love to do deep technical work. Our work is open source, cutting-edge, and industry leading.
Responsibilities:
- Develop the PT2 compiler (TorchDynamo, TorchInductor, Export, PyTorch Core).
- Improve PyTorch performance via systematic solutions for the entire community.
- Explore the intersection of the PyTorch compiler and PyTorch distributed.
- Optimize Generative AI models across the stack (pre-training, fine-tuning, and inference).
- Conduct cutting-edge research on ML compilers and ML distributed technologies.
- Collaborate with users of PyTorch to enable new use cases of PT2 technologies both inside and outside Meta.
Qualifications:
- 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 PhD degree in Computer Science, Computer Vision, Generative AI, NLP, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
- Research or industry experience in compilers, ML systems, ML accelerators, HPC, GPU performance, and similar.
- Proficient in Python or CUDA programming.
- Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment.
- Familiarity with PT2 technologies, Triton, MLIR, or experiences working inside PyTorch.
- Expert knowledge in GPU performance and writing high-performance CUDA kernels.
- Research and software engineer experience demonstrated via fellowships, patents, internships, or coding competitions.
- First-authored publications at peer-reviewed conferences (e.g., NeurIPS, MLSys, ASPLOS, PLDI, CGO, PACT, ICML, or similar).