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OpenAI

Software Engineer, Trainium

San Francisco (Remote)$295k–$380kfulltimemidAdded today

About this role

OpenAI seeks a Software Engineer to optimize AI model inference on AWS Trainium, working across kernels, compilers, and runtime systems. You'll develop high-performance software to efficiently execute frontier models on specialized hardware, collaborating with cross-functional teams to unlock accelerator capabilities.

What you'll do

  • Build and optimize OpenAI's inference stack specifically for AWS Trainium hardware
  • Develop high-performance kernels for critical model operations and workloads
  • Extend compiler support to efficiently target Trainium, improving code generation
  • Profile workloads and identify performance bottlenecks across the software stack
  • Partner with inference and ML systems teams to integrate new model architectures
  • Own end-to-end performance and systems problems from investigation through production deployment

What they're looking for

  • Systems programming and performance-critical software development
  • ML systems, compilers, or kernel development experience
  • Specialized accelerator architecture knowledge (GPU, TPU, Trainium, or similar)
  • Performance profiling and optimization across multiple abstraction layers
  • Compiler infrastructure (LLVM, MLIR, XLA, or Triton) or ML framework experience
  • AWS Trainium or AWS Neuron SDK familiarity (bonus)
  • Hardware/software co-optimization and cross-boundary problem solving
  • Experience with PyTorch, JAX, or kernel development for accelerators (bonus)
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OpenAI

OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.

View all jobs at OpenAI

Likely interview questions

  • Describe your experience optimizing software for specialized accelerators like GPUs or TPUs—what were the key performance bottlenecks you tackled?
  • Walk us through a time you owned a complex performance problem end-to-end. How did you approach profiling and identify the root cause?
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