Drimva

Meta

Compiler Engineer, Graph Compiler Performance Optimization

$184K–$257K

Bellevue, WA +2 · Full-time · Posted Aug 10, 2026

Location
Bellevue, WA
Menlo Park, CA
New York, NY
Experience
Bachelor's degree: 7+ years; Master's degree: 4+ years; PhD: 3+ years of relevant experience in compiler development or accelerating deep learning models on hardware architectures.
Education
Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field, or equivalent practical experience; alternatively, a Master's degree or PhD.

About the job

Develop and optimize MTIA graph compiler passes that improve performance for AI workloads, collaborating with researchers and hardware and compiler teams.

What you'll do

  • Design, implement, and validate graph-level compiler optimizations for the PyTorch Inductor / FX IR pipeline.
  • Analyze model performance and develop fusion, memory, and tooling improvements to reduce latency and increase hardware utilization.
  • Collaborate with hardware teams and AI researchers on hardware-software co-design and compiler support for new chip capabilities.

What they ask for

  • Experience with C/C++ and Python programming.
  • Experience in compiler development, performance optimization, or accelerating deep learning models on hardware architectures.
  • Experience with graph-level compiler optimizations and performance profiling and analysis.
  • Experience with AI hardware accelerators and deep learning frameworks.

Nice to have

  • Experience with PyTorch internals and compilation stack (TorchDynamo, FX IR, Inductor), or similar frameworks such as XLA, TVM, MLIR, or Glow.
  • Experience implementing responsible AI practices and integrating AI tools to improve workflows.

Pay

$183,997/year to $257,000/year USD base salary

Tools

C/C++, Python, PyTorch, TorchDynamo, FX IR, Inductor, XLA, TVM

AI focus

Optimize graph compilation and performance for AI workloads on MTIA hardware; support emerging model architectures and collaborate on model and compiler co-design.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 4, 2026 (UTC)