ML Compiler Engineer, Tpu Performance Optimizations

Google Google · Big Tech · Bengaluru, Karnataka, India

ML Compiler Engineer focused on optimizing the TPU compiler for machine learning workloads, including LLMs, to maximize efficiency and performance for internal and external Google Cloud customers.

What you'd actually do

  1. Learn and build an intuitive understanding of various parts of Google’s ML stack: Frameworks (e.g., JAX, PyTorch), XLA and runtime stack.
  2. Research and develop the novel compiler optimizations targeting ML workloads, and emerging architectures.
  3. Identify opportunities to improve the efficiency of the ML workloads through insightful performance debugging for ML workloads and custom kernels, and build compiler solutions to deliver those improvements.
  4. Provide technical leadership and mentorship as a Team Lead (TL), and explore strategic initiatives.

Skills

Required

  • software development
  • software design and architecture
  • machine learning
  • compilers
  • computer architecture
  • GPU programming
  • C++
  • Python

Nice to have

  • open-source software development
  • releasing and supporting open-source projects
  • state-of-the-art ML compilers and their internals
  • writing compiler optimization passes
  • accelerator Hardware (HW) architectures (TPUs/GPUs)
  • debugging correctness and performance issues at all levels of the ML Software (SW) stack
  • GPU or TPU performance analysis

What the JD emphasized

  • ML compiler optimizations
  • TPU performance
  • LLM development
  • ML platform
  • compiler optimizations
  • performance debugging
  • ML workloads
  • custom kernels
  • technical leadership

Other signals

  • ML compiler optimizations
  • TPU performance
  • LLM development
  • ML platform