Software Engineer, Systems ML - Frameworks / Compilers / Kernels

Part-time

Salem, OR

About the position

The Software Engineer, Systems ML - Frameworks / Compilers / Kernels role is part of the MTIA Software team at Meta, focusing on developing a comprehensive AI Compiler strategy. This position involves working on the PyTorch AI framework, optimizing deep learning models for specialized hardware architectures, and collaborating with AI researchers and hardware design teams to enhance performance and deployment of AI training and inference platforms.

Responsibilities

• Development of software stack focusing on AI frameworks, compiler stack, and high-performance kernel development.
• Contribute to the development of PyTorch AI framework core compilers for new AI hardware accelerators.
• Analyze deep learning networks and implement compiler optimization algorithms.
• Collaborate with AI research scientists to accelerate deep learning models in various domains 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.
• Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field, or equivalent practical experience.
• 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 12+ years of experience in AI framework development or accelerating deep learning models on hardware architectures.
• A Master's degree in Computer Science, Computer Engineering, or a relevant technical field with 8+ years of experience in AI framework development or accelerating deep learning models on hardware architectures.
• A PhD in Computer Science, Computer Engineering, or a relevant technical field with 7+ years of experience in AI framework development or accelerating deep learning models on hardware architectures.
• 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.