Software Engineer, Systems ML - PyTorch Compiler, PyTorch Framework, PyTorch Performance

Job Description

About the Role

This role is about developing the core PyTorch 2.0 technologies, innovating and advancing the state-of-the-art of ML compilers, and accelerating PT2 adoption through direct engagements with OSS and industry 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 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 technical work. Our work is open source, cutting-edge, and industry leading.

Key Responsibilities

Developing the core PyTorch 2.0 technologies and advancing ML compiler innovations.
Engaging with open source communities and industry users to promote PT2 adoption.
Building and optimizing compiler technologies to improve performance and resource efficiency of PyTorch.
Contributing to the development of high-performance kernels and GPU/ML accelerator performance improvements.

Requirements

Advanced proficiency in Python or C++ programming.
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
4+ years of research or industry experience in developing compilers, ML systems, ML accelerators, GPU performance, or similar areas.
Experience in developing PyTorch/PT2, Triton, MLIR, JAX, XLA, TVM is a huge plus.
Experience in developing inside other ML frameworks like Caffe2, TensorFlow, ONNX, TensorRT.
Knowledge in GPU architecture, ML accelerator performance, and developing high-performance kernels.
Experience with training models, end-to-end model optimizations, or applying ML to systems.
Knowledge of communication collectives, PyTorch distributed, and parallelism.
Experience in building OSS communities and extensive social media presence in the ML Sys domain.

Nice to Have

Experience in developing inside other ML frameworks like Caffe2, TensorFlow, ONNX, TensorRT.
Experience with training models, end-to-end model optimizations, or applying ML to systems.
Experience in building OSS communities and extensive social media presence in the ML Sys domain.

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