# Senior AI Application Engineer - LLM & VLM Enablement at Axelera AI

Axelera AI is hiring a Senior AI Application Engineer - LLM & VLM Enablement. Level rates it Builds AI ●●●●; you can [apply on Axelera AI](https://jobsbylevel.com/go/93e58990-e322-40fa-b8bd-48a707285cc7?utm_medium=ai-agent).

AI Level 4, AI centrality 95 out of 100. Remote (Hybrid/Remote - Europe (incl. UK)).

## Details

- Company: [Axelera AI](https://jobsbylevel.com/companies/axelera-ai)
- AI level: AI Level 4 (score 95 out of 100)
- Location: Remote (Hybrid/Remote - Europe (incl. UK))
- Posted: October 26, 2026
- Apply: https://jobsbylevel.com/go/93e58990-e322-40fa-b8bd-48a707285cc7?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-ai-application-engineer-llm-vlm-enablement-at-axelera-ai-d95c23

## Prepare for this job

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## Description

About Us 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 AI Application Engineer in the Model Enablement team (Application Software), you will own LLM and VLM support on Metis and Europa AIPUs, from studying new models to shipping them in the Voyager SDK. The role combines LLM/VLM knowledge with solid software engineering. 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: LLM/VLM study: Study new LLM and VLM families and architectures (for example mixture-of-experts, small VLMs, new attention and decoding schemes). Prototype and profile them on Metis and Europa, and decide how they should run on our hardware. Productization: Turn your results and our ML research group's results into supported Voyager SDK features, with stable APIs, tests, documentation and release gates. Model-side contracts: For each LLM/VLM building block, own the PyTorch reference with quantization baked in, the weight mapping, and one contract that lists every input, output, weight and persistent state (such as a paged KV cache), and hand that contract to the compiler team. The runtime and tests use the same definition. Pipeline integration: Bring LLM and VLM serving into the Voyager SDK pipeline builder: prefill and decode, KV-cache management, streaming text output and multimodal inputs. Quality: With the regression and quantization owners, measure accuracy against the floating-point reference, and close or explain each gap. LLM/VLM bring-up platform: Extend the model enablement platform for LLM and VLM bring-up: environments with access to the compiler, runtime and real hardware (for example through MCP tools), closed-loop runs that apply a change, measure accuracy and speed against the reference and repeat, and evaluations that score each run. GenAI direction: Recommend what the Voyager SDK needs for GenAI, agents and speech on edge devices, and which model zoo demos show it best. Qualifications: Essential 5+ years of software engineering experience, with at least 2 years on ML inference, ML systems or ML tooling. Solid understanding of transformer inference: attention variants, KV caching, prefill versus decode, sampling, tokenization, and how a VLM joins a vision encoder to a language model. Working knowledge of LLM quantization (weight-only and weight-activation schemes, calibration, outlier handling), and the ability to tell a quantization loss from an implementation bug. Strong Python and PyTorch, and experience shipping production-quality software. Daily use of AI coding agents on real codebases, with good judgment about when to verify or reject their output. Ability to turn research code and hardware constraints into a practical design. Preferred Experience with inference engines or serving stacks (for example llama.cpp, vLLM, ONNX Runtime GenAI, ExecuTorch). Experience with kernel development or a tensor compiler (for example

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