Software Engineer, Systems ML - Compilers/Kernels
Listed on 2026-10-13
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Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Software Engineer
Summary:
In this role, you will be a member of the MTIA (Meta Training & Inference Accelerator) Software team and part of the bigger PyTorch AI framework organization. MTIA Software Team has been developing a comprehensive AI Compiler strategy that delivers a highly flexible platform to train & serve new DL/ML model architectures, combined with auto-tuned high performance for production environments across specialized hardware architectures.
The compiler stack, DL graph optimizations, and kernel authoring for specific hardware, directly impacts performance and deployment velocity of both AI training and inference platforms will be working on one of the core areas such as PyTorch framework components, AI compiler and runtime, high-performance kernels and tooling to accelerate machine learning workloads on the current & next generation of MTIA AI hardware platforms.
You will work closely with AI researchers to analyze deep learning models and lower them efficiently on MTIA hardware. You will also partner with hardware design teams to develop compiler optimizations for high performance. You will apply software development best practices to design features, optimization, and performance tuning techniques. You will gain valuable experience in developing machine learning compiler frameworks and will help in driving next generation hardware software codesign for AI domain specific problems.
Required Skills:
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
Analyze deep learning networks, develop & implement compiler optimization algorithms
Collaborating 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
Minimum Qualifications:
Minimum Qualifications:
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. Degree must be completed prior to joining Meta
Proven C/C++ programming skills
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
Preferred Qualifications:
Preferred Qualifications:
Experience working with frameworks like PyTorch, Caffe2, Tensor Flow, ONNX, TensorRT
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
Knowledge of GPU, CPU, or AI hardware accelerator architectures
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
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
OR AI high performance…
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