Staff AI Application Engineer - Heterogeneous Computing

This is your chance to shape and be part of a dynamic, fast-growing, international organization. We offer an attractive compensation package, including a pension plan, extensive employee insurances and the option to get company shares.

An open culture that supports creativity and continual innovation is awaiting you. Collaborative ownership and freedom with responsibility is characteristic for the way we act and work as a team.

At Axelera AI, we wholeheartedly embrace equal opportunity and hold diversity in the highest regard. Our steadfast commitment is to cultivate a warm and inclusive environment that empowers and celebrates every member of our team. We welcome applicants from all backgrounds to join us in shaping the future of AI.

As a Staff AI Application Engineer in the Model Enablement team (Application Software), you will build our model enablement platform and decide how each model runs across the Metis or Europa AIPU, the host CPU and the host's accelerators. How this work is split and scheduled determines most of the end-to-end performance a customer sees in the Voyager SDK.

The role combines architecture and hands-on work across the model frontend, the graph compiler, the runtime and the application pipeline. During model bring-up, you cover the frontend and graph side, and a device-side engineer covers the backend.

Our team uses AI coding agents in daily work, and you will use them to build, test and verify your own work. We also welcome experienced field application engineers, technical leads and engineering managers who want to return to hands-on engineering. Experience in planning, delegating and reviewing work transfers well to working with AI agents.

• Model enablement platform and agent harness: Build the platform that engineers and AI agents use to bring up models: reusable tools to partition, compile, run and profile; environments with access to real hardware; closed-loop runs that apply a change, measure it against the reference and repeat; and verification (differential testing, regression detection) integrated into CI.

• Model ingestion: Own how a PyTorch or ONNX model is captured, analysed and split across the AIPU's matrix and RISC-V vector units, the host CPU and the host's accelerators (GPU, NPU, video decoder), with placement tags that users can read and override. Build this with the graph compiler team so it works for every model.

• Heterogeneous execution: Extend that split to large models, cascades and multi-model pipelines, for example with expert offloading and caching across device and host memory. Agree the plan with the graph compiler team.

• End-to-end performance: With the application team (pipeline builder) and the runtime team, bring each model to its best end-to-end performance: overlap of host and device work, zero-copy buffers, batching, multi-core scheduling, and models with several inputs. Profile device time, host time and data movement, and confirm each speedup in the full pipeline.

• Critical models: Study the models that matter most to customers, on Metis today and on Europa, and turn each lesson into a platform improvement.

We offer a flexible working arrangement, with options to:

• Work from one of our Axelera AI offices (Leuven in Belgium, Amsterdam and Eindhoven in the Netherlands, Zurich in Switzerland, Florence and Milan in Italy or Bristol in the United Kingdom) if you're already based in the vicinity.

• Work fully remotely from any European country (incl. the UK) you are already in.

• Relocate with us and work from Italy (Florence or Milan) or the Netherlands (Amsterdam or Eindhoven).

Kindly note that priority will be given to candidates who are [interested in being] based in Belgium or Italy.