Axelera AI is not your average deep-tech company. We're building the next-generation AI platform to empower anyone working to advance humanity and improve the world around us.

We aim to lead the democratization of Artificial Intelligence, offering faster, easy-to-use AI acceleration while minimizing power and cost — bringing AI inference from the cloud to the edge, and now to the data center as well.

Founded in July 2021 and headquartered at the High Tech Campus in Eindhoven, the Netherlands, Axelera AI has grown to a team of over 250 engineers, developers, and business experts across offices in the Netherlands, Belgium, Switzerland, Italy, and the UK. We're advised by some of the brightest minds in AI and backed by leading global and institutional investors, having raised over $450 million in total funding to date.

Since our founding, we've taped out two chips and launched our Metis® AI Platform - a hardware and software solution that, together with our adaptable Voyager® SDK, makes deploying machine learning models effortless. We've since expanded our roadmap with the Europa™ AIPU.

Today, we're focused on growing our solutions and our ecosystem — driving the democratization of AI toward a greener, fairer, and safer world.

 

Below are the open positions:

1. Intern – ML Inference Performance Engineer

 

1- Intern – ML Inference Performance Engineer

Location: Hybrid, Eindhoven

Compensation: 1000-1500 EUR gross p/m
Role Overview
We're looking for a curious, rigorous engineer to join our team and dig into the performance of ML inference systems. You'll work across the full inference stack — from model export and compiler toolchains to runtime execution on silicon — to build a clear, evidence-based picture of how different platforms perform and why. Your work will go beyond running benchmarks: you'll develop a repeatable evaluation methodology, investigate performance bottlenecks at the hardware and software level, and build the tooling that transforms raw measurements into actionable insight. The findings you produce can directly shape our product decisions.
Responsibilities

• Benchmarking & Tooling: Develop a thorough understanding of internal benchmarking tools covering throughput, latency, power, and accuracy across device-level, host-transaction, and end-to-end pipeline scenarios. Improve existing tooling, define reproducible procedures, and establish a standardised results format for rigorous cross-platform comparisons. Maintain a dedicated dashboard for performance visualisations.
• Platform Evaluation: Research and evaluate AI accelerator products from various vendors, gaining hands-on experience with their SDKs, toolchains, flexibility, and limitations through a structured evaluation process. Track model support across platforms to identify strengths, gaps, and areas for improvement.
• Pipeline Analysis: Characterise full inference pipelines, capturing host-device transaction overhead and end-to-end performance metrics. Ensure equivalent pipeline configurations across platforms using frameworks such as GStreamer to maintain methodological consistency.
• Lab & Infrastructure: Set up and maintain lab hosts across multiple hardware platforms and support the onboarding of new evaluation hardware.
• Reporting: Synthesise findings into clear, structured reports that directly inform engineering and roadmap decisions.

Requirements

• Currently enrolled in the final years of a Bachelor's programme or in a Master's programme in Computer Engineering, Electrical Engineering, Computer Science, or a related field. This position may also be carried out as a Master's thesis project.
• Solid understanding of machine learning concepts and familiarity with common model architectures (CNNs, transformers, etc.).
• Hands-on experience with Python and C++, including the ability to work with and compare pythonic and C++ APIs.
• Familiarity with Linux environments and command-line tooling.
• Understanding of hardware-software interaction, including concepts such as memory bandwidth, compute utilization, and power consumption.
• Strong analytical mindset with the ability to design controlled experiments and draw relevant conclusions from data.
• Proficient written and verbal communication skills in English, with the ability to document findings clearly and precisely.
• Good organizational skills
• Proficient in English

Preferred Qualifications

• Experience with ML inference frameworks, streaming analytic toolkits (like DeepStream) and runtimes (e.g., ONNX, TensorRT, PyTorch, OpenVINO).
• Familiarity with hardware accelerators such as GPUs, NPUs, or AI processing units.
• Experience with multimedia or inference pipeline frameworks such as GStreamer.
• Exposure to performance profiling and benchmarking tools.
• Experience with data visualization tools, particularly Grafana.
• Familiarity with embedded or edge computing platforms.
• Prior experience in a research or industrial internship involving performance analysis or hardware evaluation.