Software Development Manager - Compiler, Aws Neuron, Annapurna Labs

Amazon Amazon · Big Tech · Cupertino, CA · Software Development

Seeking a Software Engineering Manager to lead a team developing compiler optimization algorithms and deploying a new compiler for AWS custom hardware (Inferentia and Trainium chips). The role involves technical leadership, mentoring, and partnering with AWS ML services teams to improve deep learning model performance and productivity.

What you'd actually do

  1. leading a team of experienced compiler engineers developing compiler optimization algorithms and deploying, at scale, a new compiler targeting AWS custom hardware.
  2. technically capable, credible, and curious in your own right as a trusted AWS Neuron Manager, innovating on behalf of our customers.
  3. leverage your technical communications skill as a hands-on partner to AWS ML services teams, involved in pre-silicon design, bringing new products/features to market.
  4. build the software that will boost the entire deep learning community.
  5. You will have deep knowledge of resource management, scheduling, code generation, optimization, and new instruction architectures including CPU, NPU, GPU and novel forms of compute.

Skills

Required

  • 5+ years of engineering team management experience
  • 9+ years of working directly within engineering teams experience
  • 4+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience
  • Experience partnering with product or program management teams
  • Deep understanding of compilers (resource management, instruction scheduling, code generation, and compute graph optimization)
  • Strong software design fundamentals
  • excellent system-level coding skills with an emphasis on graph theory and performance techniques

Nice to have

  • PhD in computer science, computer engineering, or related field, or MS degree
  • Experience with general troubleshooting/debugging of hardware
  • experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
  • Experience with XLA, TVM, MLIR, LLVM, deep learning models and algorithms, and deep learning framework design.
  • Interactions with open-source communities, in either a leadership or code contributor role

What the JD emphasized

  • compiler optimization algorithms
  • deploying, at scale, a new compiler targeting AWS custom hardware
  • deep knowledge of resource management, scheduling, code generation, optimization, and new instruction architectures

Other signals

  • AWS Machine Learning accelerators
  • Inferentia chip
  • Trainium chip
  • AWS Neuron Software Development Kit (SDK)
  • ML compiler
  • runtime
  • PyTorch
  • JAX
  • deep learning community
  • custom hardware
  • deep learning models
  • compiler optimization algorithms
  • inference performance
  • training performance