Job description

The MTIA (Meta Training & Inference Accelerator) Software team is part of the AI & Compute Foundation org. The team’s mission is to explore, develop and help productize high-performance software and hardware technologies for AI at datacenter scale. The team co-optimizes both SW (e.g., algorithms and numerics) and HW (e.g., platform and network) to come up with balanced system design. To develop new systems, requires understanding performance bottlenecks on existing systems. As a result, the team invests significantly into optimizing AI production models on existing systems. This has resulted in TCO wins for all key AI services. The team has been developing AI frameworks to accelerate Meta’s DL/ML workloads on the specialized MTIA AI accelerator hardware in a highly performant and flexible way. As part of the AI acceleration software stack, we develop kernel libraries exploiting various hardware architectural features, achieving high performance for our inference and training workloads. Our team at Meta AI offers twelve (12) to twenty-four (24) weeks long internships and we have various start dates throughout the year. 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 Proven C/C++, Python or Rust programming skills Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment OR AI Compiler: Experience with compiler optimizations such as loop optimizations, vectorization, parallelization, hardware specific optimizations such as SIMD. Experience with MLIR, LLVM, IREE, XLA, TVM, Halide is a plus OR AI Frameworks: Experience in developing training and inference framework components, such as SGLang, vLLM, PyTorch, Caffe2, TensorFlow, ONNX, TensorRT OR AI high performance kernels: Experience with 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 Knowledge of GPU, CPU, or AI hardware accelerator architectures 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 ICLR Experience working and communicating cross functionally in a team environment Intent to return to a degree-program after the completion of the internship/co-op

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Keywords to mirror

aimlmachine learningpythonrust

Platform: meta
Effort: medium

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