Staff AI Application Engineer – Heterogeneous Computing

Axelera AI

. Build the model enablement platform and agent harness used by engineers and AI agents to bring up models .

Posted 10/26/2026full-timeRemote • BelgiumLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in systems engineering, compiler design, and performance optimization, with a strong focus on heterogeneous computing and model execution. Proficient in C++ and Python, with hands-on experience in AI coding agents and ML graph representations.

Highest-signal resume keywords

Heterogeneous ComputingC++ ProgrammingPython ProgrammingMLIR or Compiler Framework ExperienceSystem-Level Design Ownership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Performance OptimizationParallel ProgrammingComputer Architecture FundamentalsData Movement AnalysisModel PartitioningInference Runtime ExperienceGPU Compute OptimizationSIMD OptimizationModel Execution TechniquesAI Coding Agents

Tools & Technologies

PyTorchONNXAI AgentsMetisEuropa

Industry Keywords

Systems EngineeringCompilersRuntimesPerformance EngineeringHeterogeneous Execution

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Build the model enablement platform and agent harness used by engineers and AI agents to bring up models
Develop reusable tools to partition, compile, run and profile models
Provide real-hardware environments and closed-loop runs that measure changes against references
Integrate differential testing and regression detection into CI
Own how PyTorch or ONNX models are captured, analysed and split across AIPU units, host CPU and host accelerators
Build placement tags that users can read and override
Extend heterogeneous execution to large models, cascades and multi-model pipelines
Optimize end-to-end performance through host/device overlap, zero-copy buffers, batching, multi-core scheduling and multi-input models
Profile device time, host time and data movement and validate speedups in the full pipeline
Study critical customer models on Metis and Europa and convert findings into platform improvements
Collaborate with graph compiler, application and runtime teams

Requirements

What you’ll need

8+ years of engineering experience in systems, compilers, runtimes or performance, including system-level design ownership
Hands-on experience with heterogeneous computing across CPU, GPU, DSP, NPU or FPGA
Ability to reason about compute, memory bandwidth, latency and data movement
Solid fundamentals in computer architecture, operating systems and parallel programming
Familiarity with ML graph representations and compiler concepts, including graph capture, lowering, operator fusion, partitioning and memory planning
Strong C++ and Python
Daily use of AI coding agents on real engineering work, with judgment to review and test their output
Hands-on experience with MLIR or another compiler framework preferred
Experience with an inference runtime or execution engine preferred
Experience with GPU compute or SIMD optimization on Arm or x86 preferred
Experience with large-model execution techniques preferred
Experience building agent environments, evaluation harnesses or verification frameworks preferred

Benefits

Comp & perks

Pension plan
Extensive employee insurances
Option to get company shares
Flexible working arrangement
Open culture supporting creativity and continual innovation
Collaborative ownership and freedom with responsibility
Equal opportunity and inclusive environment