Software Engineer, Systems ML – Frameworks / Compilers / DL-Kernels in Toronto

In this role, you will be a member of the MTIA (Meta Training & Inference Accelerator) Software team and part of the bigger AI and Compute Foundations team. MTIA Software Team has been developing a comprehensive software platform strategy that delivers a highly flexible platform to train & serve new deep learning model architectures, combined with auto-tuned high performance for production environments across specialized hardware architectures.

You will be working on one of the core areas such as PyTorch & GenAI framework components, AI compiler and runtime, high-performance deep learning 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 optimize them efficiently on MTIA hardware. You will also partner with hardware design teams to develop 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.

Responsibilities:

• Development of SW stack with one of the following core focus areas: AI frameworks, compiler stack, high performance deep learning kernel development, runtime and tooling for MTIA accelerator.
• Contribute to the development of the industry-leading PyTorch AI framework, core compilers, and GenAI OSS frameworks to support new state-of-the-art inference and training AI hardware accelerators and optimize their performance.
• Analyze deep learning models, develop and implement optimization algorithms towards high performance and efficiency gains.
• Collaborate with AI research scientists to accelerate the next generation of deep learning models such as recommendation systems and Generative AI.

Minimum Qualifications:

• 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:

• Experience working with frameworks like PyTorch, TensorFlow, ONNX, TensorRT, and GenAI Frameworks such as vLLM or SGLang.
• 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.
• Runtime development and system performance optimizations such as runtime analysis of latency, memory bandwidth, I/O access, compute utilization analysis and associated tooling development.
• 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.
• Knowledge of GPU, CPU, or AI hardware accelerator architectures.
• 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.
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements).