Software Engineer, Systems ML - Frameworks / Compilers / Kernels

About the position

The Software Engineer, Systems ML - Frameworks / Compilers / Kernels role at Meta involves working within the MTIA Software team to develop a comprehensive AI Compiler strategy that enhances the PyTorch AI framework. The position focuses on creating a flexible platform for training and serving deep learning models, optimizing performance for production environments across specialized hardware architectures. The engineer will collaborate with AI researchers and hardware design teams to implement compiler optimizations and performance tuning techniques, contributing to the advancement of machine learning compiler frameworks and next-generation hardware software co-design.

Responsibilities

• Development of software stack focusing on AI frameworks, compiler stack, and high-performance kernel development for next-generation hardware architectures.
• Contribute to the development of PyTorch AI framework core compilers to support new AI hardware accelerators and optimize their performance.
• Analyze deep learning networks and implement compiler optimization algorithms.
• Collaborate with AI research scientists to accelerate deep learning models in areas such as Recommendation systems, Generative AI, Computer vision, and NLP.
• Performance tuning and optimizations of deep learning framework and software components.

Requirements

• Proven C/C++ programming skills.
• Currently pursuing or in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field, with the degree completed prior to joining Meta.
• Experience in AI framework development or accelerating deep learning models on hardware architectures.

Nice-to-haves

• A Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field with 4+ years of experience in AI framework development or accelerating deep learning models on hardware architectures, or a Master's degree with 2+ years of experience, or a PhD in a relevant field.
• Knowledge of GPU, CPU, or AI hardware accelerator architectures.
• Experience with frameworks like PyTorch, Caffe2, TensorFlow, ONNX, TensorRT.
• Experience with CUDA programming, OpenMP/OpenCL programming, or AI hardware accelerator kernel programming.
• Experience with compiler optimizations such as loop optimizations, vectorization, and parallelization, as well as familiarity with MLIR, LLVM, IREE, XLA, TVM, Halide.

Benefits

• Bonus
• Equity
• Health benefits