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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. Key responsibilities: - 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.
Qualifications: Essential - 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, including reasoning 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: graph capture (torch.export, FX, ONNX), lowering, operator fusion, partitioning, memory planning. - Strong C++ and Python. - Daily use of AI coding agents on real engineering work, with the judgment to review and test their output. Preferred - Hands-on experience with MLIR or another compiler framework (TVM, XLA, TorchInductor, IREE). - Experience with an inference runtime or execution engine (ONNX Runtime execution providers, a custom NPU or DSP runtime). - Experience with GPU compute (CUDA, OpenCL, Vulkan) or SIMD optimization on Arm or x86. - Experience with large-model execution techniques such as offloading, expert caching, speculative decoding, pipelining across devices. - Experience building agent environments, evaluation harnesses or verification frameworks (differential testing, regression detection), or MCP-based tooling. Location 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.
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. Axelera AI may at times work with external parties (such as recruiters, headhunters, or executive search firms) to support both publicly posted and confidential searches. Our hiring process never involves any payment from candidates, and we will never require you to use a particular paid service to be considered for a role.
If you have been approached by someone on our behalf and want to confirm it’s legitimate, please contact us directly at.
Axelera AI is not your regular deep-tech company. We are creating the next-generation AI platform to support anyone who wants to help advancing humanity and improve the world around us. In just five years, we have raised a total of $450 million and have built a world-class team of 250+ employees (including 60+ PhDs with more than 40,000 citations), both remotely from 21 different countries and with offices in Belgium, France, Switzerland, Italy, the UK, headquartered at the High Tech Campus in Eindhoven, Netherlands.
We have also launched our Metis™ AI Platform, which achieves a 3-5x increase in efficiency and performance, and have visibility into a strong business pipeline exceeding $100 million. Our unwavering commitment to innovation has firmly established us as a global industry pioneer. Are you up for the challenge?
Position Overview As a Senior/Staff Software Engineer in the Model Enablement team, 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.
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