Staff AI App Engineer - Heterogeneous Compute (Remote)

Axelera AI

Denmark

Remote

DKK 897,000 - 1,345,000

Full time

3 days ago
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Benefits offered by this job

pension plan
extensive employee insurances
option to get company shares

Job summary

Axelera AI is seeking a Staff AI Application Engineer in the Model Enablement team to build our model enablement platform and optimize how models run across Metis or Europa AIPUs, CPUs, and accelerators. You will shape frontends, graph compilers, runtimes and pipelines with a strong emphasis on performance.

Expect collaboration with AI agents, field engineers, and leadership to drive end-to-end improvements.

Qualifications

  • 8+ years of engineering experience in systems, compilers, runtimes or performance.
  • Hands‑on experience with heterogeneous computing across CPU, GPU, DSP, NPU or FPGA.
  • Solid fundamentals in computer architecture, operating systems and parallel programming.
  • Familiarity with ML graph representations and compiler concepts: graph capture, lowering, fusion, partitioning.

Responsibilities

  • Model enablement platform and agent harness: Build the platform that engineers and AI agents use to bring up models.
  • Model ingestion: Capture and split models across AIPU units and accelerators with placement tags.
  • Heterogeneous execution: Extend split to large models and multi-model pipelines across devices.
  • End-to-end performance: Optimize overlap, zero-copy buffers, batching, and scheduling.
  • Study emerging models and drive platform improvements based on customer needs.

Skills

C++
Python
Parallel programming
AI agents

Tools

PyTorch
ONNX

Job description

Axelera AI is seeking a Staff AI Application Engineer in the Model Enablement team to build our model enablement platform and optimize how models run across Metis or Europa AIPUs, CPUs, and accelerators. You will shape frontends, graph compilers, runtimes and pipelines with a strong emphasis on performance.

Expect collaboration with AI agents, field engineers, and leadership to drive end-to-end improvements.

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