About the position
This role is to develop the core PyTorch 2.0 technologies, innovate and advance the state-of-the-art of ML compilers, and accelerate PT2 adoption in research and production through engagements with OSS and internal users. 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 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. Our work is open source, cutting-edge, and industry leading.
Responsibilities
- Improve PyTorch performance via systematic solutions that benefit the entire community.
- Advance the PyTorch compiler technologies and maintain its long-term health.
- 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.
- Engage with users of PyTorch to enable new use cases of PyTorch Compiler technologies both inside and outside Meta.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
- Proficient in Python or CUDA programming.
- 2+ years of research or industry experience in developing compilers, ML systems, ML accelerators, GPU performance, and similar.
- Expert knowledge of GPU or ML accelerator performance and developing kernels/libraries targeting ML HW.
- Experience with training or serving models or end-to-end optimizations for real models.
- Experience with PT2 technologies (e.g., TorchInductor, TorchDynamo, Export) or distributed technologies (e.g., PyTorch distributed, communication collectives, parallelism).
- Experience in developing ML compilers (e.g., PyTorch Compiler, Triton, MLIR, JAX, XLA) or ML frameworks (e.g., JAX, vLLM, ONNX, TensorRT).
- Good understanding of the fast-moving Generative AI space.
- Experience in building OSS communities and extensive social media presence in the ML Sys domain.
