Intern - ML Inference Performance Engineer

Axelera
Netherlands
HybridCareer-pivot friendly

Who this role is best for

Best suited to technically curious engineers with embedded systems and AI inference experience working in a fast-evolving deep-tech environment.

Best fit for

  • Candidates with a background in embedded systems and AI inference tools
    — “Experience with end-to-end computer vision pipelines”
  • Individuals comfortable with building standardized evaluation methodologies
    — “Improve existing tooling, define reproducible procedures, and establish a standardised results format”
  • Those with a strong foundation in deep learning model concepts and benchmarking
    — “Familiarity with deep learning model concepts (quantization, ONNX, PyTorch, etc.)”

Things to consider

  • Requires hands-on lab work with multiple hardware platforms and embedded hosts
    — “Set up and maintain lab hosts across multiple hardware platforms”
  • Must be able to produce clear, structured reports for engineering teams
    — “Synthesise findings into clear, structured reports that directly inform engineering and roadmap decisions”

How to stand out

  • Highlight experience with inference SDKs and model optimization techniques
    — “Experience with inference tools, APIs, or SDKs (e.g., TensorRT)”
  • Demonstrate familiarity with Linux, Bash, and Docker in your technical projects
    — “Proficiency with Linux, Bash scripting, and Docker”
  • Emphasize your ability to document findings clearly and precisely in English
    — “Proficient written and verbal communication skills in English, with the ability to document findings clearly and precisely”
  • Demonstrate experience with embedded hosts and cross-platform pipeline consistency
    — “Ensure equivalent pipeline configurations across platforms using frameworks such as GStreamer”
Pace · SteadyCollaboration · MediumAutonomy · MediumDecision Impact · Individual

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • develop a thorough understanding of internal benchmarking tools
  • improve existing tooling
  • define reproducible procedures
  • establish a standardised results format
  • research and evaluate AI accelerator products
Typical background
final years of a Bachelor's programme or in a Master's programme in Computer Engineering, Electrical Engineering, Computer Science, or a related field

Skills & requirements

Required

Benchmarking ToolsPython DevelopmentC/c++ KnowledgeEnd-to-end Computer Vision PipelinesBenchmarking ConceptsInference ToolsApisSdksDeep Learning Model ConceptsQuantizationONNXPyTorchAgentic AIAI Accelerator ProductsToolchainsFlexibilityLimitationsModel SupportHost-device Transaction Overhead

Stack & domain

PythonC/c++End-to-end Computer Vision PipelinesBenchmarkingInference ToolsDeep Learning Model ConceptsAgentic AIResearchToolingReportingAnalysisMethodologyAIMLInference Performance

About the role

Original posting from Axelera via Ashby

About Us

Axelera AI https://www.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 $370 million and have built a world-class team of 250+ employees (including 60+ PhDs with more than 40,000 citations), both remotely from 20 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

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.

Key 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.
  • Python development experience
  • C/C++ knowledge
  • Experience with end-to-end computer vision pipelines
  • Familiarity with benchmarking concepts (performance, latency, etc.)
  • Experience with inference tools, APIs, or SDKs (e.g., TensorRT)
  • Familiarity with deep learning model concepts (quantization, ONNX, PyTorch, etc.)
  • Development experience using agentic AI
  • Knowledge of version control (Git)
  • Familiarity with LLM benchmarking concepts
  • Proficiency with Linux, Bash scripting, and Docker
  • Hands-on experience with embedded hosts
  • Proficient written and verbal communication skills in English, with the ability to document findings clearly and precisely.
  • Good organizational skills

Nice to have:

  • GStreamer knowledge
  • Basic GUI design experience

Location

Work from our Axelera AI office in Eindhoven (Netherlands).

What we offer 

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.

Source: Axelera careers (Ashby)

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