Senior AI Application Engineer – LLM, VLM Enablement

Axelera AI

. Own LLM and VLM support on Metis and Europa AIPUs, from studying new models to shipping them in the Voyager SDK .

Posted 10/26/2026full-timeRemote • NetherlandsSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in LLM and VLM support, including model prototyping, quantization, and integration into production environments. Proficient in Python and PyTorch, with a strong focus on delivering production-quality software and leveraging AI coding agents for efficient development.

Highest-signal resume keywords

LLM And VLM SupportPython ProgrammingPyTorch FrameworkML Inference SystemsTransformer Inference

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Hard Skills

Software EngineeringML ToolingQuantization TechniquesModel ProfilingTokenizationAttention VariantsKV CachingSamplingVLM IntegrationProduction-Quality Software

Soft Skills

Judgment In Code VerificationPractical Design Implementation

Tools & Technologies

Voyager SDKAI Coding AgentsInference EnginesLlama.cppVLLMONNX Runtime GenAIExecuTorchTritonTVMMLIR

Industry Keywords

GenAIEmbedded DevicesSpeech ApplicationsEvaluation HarnessesTesting Harnesses

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Own LLM and VLM support on Metis and Europa AIPUs, from studying new models to shipping them in the Voyager SDK
Study new LLM and VLM families and architectures, prototype and profile them on Metis and Europa, and determine how they should run on the hardware
Turn research results into supported Voyager SDK features with stable APIs, tests, documentation and release gates
Own PyTorch references with quantization, weight mappings, and contracts covering inputs, outputs, weights and persistent state
Hand model-side contracts to the compiler team and ensure runtime and tests use the same definitions
Integrate LLM and VLM serving into the Voyager SDK pipeline builder, including prefill, decode, KV-cache management, streaming text output and multimodal inputs
Measure accuracy against floating-point references and close or explain gaps
Extend the model enablement platform for LLM and VLM bring-up, including compiler/runtime/hardware environments, closed-loop runs and evaluations
Recommend Voyager SDK capabilities for GenAI, agents and speech on edge devices, and identify representative model-zoo demos
Use AI coding agents to build, test and verify work

Requirements

What you’ll need

5+ years of software engineering experience
At least 2 years on ML inference, ML systems or ML tooling
Solid understanding of transformer inference, including attention variants, KV caching, prefill versus decode, sampling, tokenization, and VLM vision-language integration
Working knowledge of LLM quantization, including weight-only and weight-activation schemes, calibration, and outlier handling
Ability to distinguish quantization loss from an implementation bug
Strong Python and PyTorch skills
Experience shipping production-quality software
Daily use of AI coding agents on real codebases, with judgment to verify or reject their output
Ability to turn research code and hardware constraints into practical designs
Preferred: experience with inference engines or serving stacks such as llama.cpp, vLLM, ONNX Runtime GenAI, or ExecuTorch
Preferred: kernel development, tensor compilers such as Triton, TVM or MLIR, or profiling models on accelerator hardware
Preferred: speech (ASR, TTS) or agentic applications on embedded devices
Preferred: evaluation or testing harnesses for ML systems or AI agents, or tool-calling agents such as MCP servers

Benefits

Comp & perks

Attractive compensation package
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, warm and inclusive environment