Engineering and infrastructure

AI compilers, kernels and runtime systems

AI compilers, kernels and runtime systems is the work of turning model computation into fast, efficient operations on real hardware. AI companies need it because the gap between a model on paper and a model that runs affordably is large.

Models keep growing, and serving them costs real compute. Companies now want engineers who can close the gap between what the hardware can do and what the model actually uses. Hiring is tricky because the skills sit in compilers, systems programming and accelerator performance, and few people span them. Titles vary widely. A strong hire shows measured speedups and can explain where the time went.

Hiring now 3Emerging 1

Roles in this family

4 roles

Hiring nowAI Inference Compiler EngineerAn AI Inference Compiler Engineer makes trained models run faster and cheaper on real hardware. They change how model computation is compiled and…Hiring nowGPU Kernel EngineerA GPU Kernel Engineer writes and tunes the low-level code that runs model operations on accelerators. Faster, correct kernels cut compute cost and…Hiring nowML Runtime EngineerAn ML Runtime Engineer builds the scheduling, memory and execution layer that runs models on real hardware. Their work decides whether a model…EmergingAI Performance Portability EngineerAn AI Performance Portability Engineer keeps models fast and accurate across different hardware. They cut dependence on one chip or vendor, which…

Where the people come from

Compilers, systems programming or accelerator performance

Not to be confused with. Architecture research and data-centre facilities are distinct families.

Core skills

  • Linear algebra and optimisation
  • Distributed systems
  • GPU programming
  • Hardware and computer architecture

Where it is used

  • Cross-sector AI platforms
  • Semiconductors and compute infrastructure

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