Principal Software Development Engineer – Compiler & ML Acceleration

AMD AMD · Semiconductors · MA · Engineering

This Principal Software Development Engineer role focuses on developing compiler technology and MLIR-based infrastructure to optimize the performance of AI workloads, specifically LLMs, on AMD's accelerator architectures. The role involves designing and implementing compiler flows, optimizing ML inference, and working cross-functionally to align compiler capabilities with hardware.

What you'd actually do

  1. Lead architecture design and development of compiler components and optimization pipelines for machine learning
  2. Design and implement MLIR-based compiler flows to lower high-level ML representations into highly optimized hardware-specific code
  3. Drive model compilation and data movement optimization for ML inference workloads
  4. Define and implement compiler strategies for operator fusion, memory planning, scheduling, and performance optimization
  5. Work cross-functionally with hardware, runtime, frontend, and systems teams to align compiler capabilities with evolving accelerator architectures

Skills

Required

  • compiler development (front-end, middle-end, and/or back-end)
  • MLIR and/or LLVM-based compiler infrastructure
  • neural network workloads
  • graph-level and compiler-level optimizations for ML models
  • C++ development skills
  • large, complex codebases
  • Principal-level technical scope
  • targeting or optimizing for NPUs or specialized AI accelerators
  • model compilation stacks
  • custom lowering pipelines
  • Contributions to compiler or ML infrastructure in production environments

Nice to have

  • compiler technology
  • MLIR-based infrastructure
  • model-to-hardware optimization
  • AI workloads
  • specialized compute engines
  • operator fusion
  • memory planning
  • scheduling
  • performance optimization
  • technical leadership
  • mentorship
  • long-term roadmap decisions

What the JD emphasized

  • strong focus on enabling high-performance execution for AI workloads
  • Proven experience working on neural network workloads
  • Deep understanding of graph-level and compiler-level optimizations for ML models
  • Experience targeting or optimizing for NPUs or specialized AI accelerators
  • Contributions to compiler or ML infrastructure in production environments

Other signals

  • accelerate Large Language Models (LLMs) and ML workloads on emerging accelerator architectures
  • enabling high-performance execution for AI workloads
  • intersection of compiler development, ML frameworks, and AI model execution
  • Drive model compilation and data movement optimization for ML inference workloads
  • Define and implement compiler strategies for operator fusion, memory planning, scheduling, and performance optimization