研究科学家实习生，AI与计算基础 - MTIA软件

Meta（Meta Platforms）岗位：Research Scientist Intern, AI & Compute Foundation - MTIA SW (PhD) Responsibilities

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

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

Analyze deep learning networks, develop & implement compiler optimization algorithms.

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

Performance tuning and optimizations of deep learning framework & software components.

In the process of obtaining, PhD degree in the field of Computer Science or a related STEM field

Research experiences in one or more of: AI compilers, high-performance kernel development, deep learning framework and runtime internals, numerics and low-precision formats, or parallelism strategies for large-scale training and inference

Knowledge of machine learning systems and computer architecture, including the memory and storage hierarchy, parallel execution, and performance analysis on accelerated hardware

Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

SIMD.

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

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

Track record of achieving results as demonstrated by publications at leading systems conferences such as MLSys, OSDI, ASPLOS, ISCA, or CGO, or systems-focused work at ML venues such as NeurIPS, ICML, or

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Currently pursuing a PhD degree in Computer Science or a related STEM field.

Research experience in one or more of: AI compilers, high-performance kernel development, deep learning framework and runtime internals, numerics and low-precision formats, or parallelism strategies for large-scale training