系统机器学习编译器软件工程师

Meta（Meta Platforms）岗位：Software Engineer, Systems ML - Compilers / Kernels Responsibilities

Development of the SW stack with one of the following core focus areas: AI compiler stack, frameworks, high-performance kernel development and acceleration onto the next generation of hardware architectures

Contribute to the development of the PyTorch AI framework core compilers to support new state of the art inference and training AI hardware accelerators and optimize their performance

Collaborating with AI research scientists to accelerate the next generation of deep learning models such as Recommendation systems, Generative AI, Computer vision, NLP etc

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.

Experience in AI framework development or accelerating deep learning models on hardware architectures

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

OR AI frameworks: Experience in developing training and inference framework components.

Experience in system performance optimizations such as runtime analysis of latency, memory bandwidth, I/O access, compute utilization analysis and associated tooling development

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

A Bachelor's degree in Computer Science, Computer Engineering, relevant technical field and 7+ years of experience in AI framework development or accelerating deep learning models on hardware architectures OR a Master's degree in Computer Science, Computer Engineering, relevant technical field and 4+ years of experience in AI framework development or accelerating deep learning models on hardware architectures OR a PhD in Computer Science, Computer Engineering, or relevant technical field and 3+ years of experience in AI framework development or accelerating deep learning models on hardware architectures

SIMD.

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

CUDA programming, OpenMP / OpenCL programming or AI hardware accelerator kernel programming.

Experience in accelerating libraries on AI hardware, similar to cuBLAS, cuDNN, CUTLASS, HIP, ROCm etc

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Currently has or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience;

degree must be completed prior to joining.

Proven C/C++ programming skills.