Opportunities · Part-time opportunity
Working Student - Data Engineering
Kyrall · Munich, Germany
Role overview
What you'll own
- You'll work on our data team that owns the data our models learn from.
- Develop parsers that turn CAD formats into ML-compatible representations
- Source, clean and structure 3D parametric datasets
- Maintain and extend our data pipeline
Requirements and eligibility
- Eligibility requirements are not available in the listing details we have.
Preferred, not required
- Experience with CAD software or libraries
- Familiarity with data pipeline tooling
- Why Kyrall?
Dates for this one
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Posted
Sep 23, 2026
Applications close
Nov 5, 2026
43 days left
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Read the full listing context
We're building the world's fastest way to design real, functional 3D parts. Kyrall's AI-powered design agent turns ideas into ready-to-manufacture 3D models in seconds.
Backed by top European VCs, we're a young, well-funded startup based in Munich — and we're just getting started. The Role You'll work on our data team that owns the data our models learn from.
That means sourcing 3D parametric data, writing parsers for CAD formats, and keeping the pipeline running so the AI team always has clean, usable training data.
What To Expect • Develop parsers that turn CAD formats into ML-compatible representations • Source, clean and structure 3D parametric datasets • Maintain and extend our data pipeline • Deliver datasets to the AI team and support their experiments Your Profile • Studying computer science, engineering, mathematics or similar • Solid Python skills • Interest in 3D geometry, CAD or geometric data (B-Reps, meshes, point clouds) • Structured, self-driven way of working • 20 hours per week, on-site in Munich Nice To Have • Experience with CAD software or libraries • Familiarity with data pipeline tooling Why Kyrall?
We're reinventing how machines are designed and built, from the ground up. 3D modeling tools were the last major leap in this space, and the way we design parts today has hit its limit.
You'll work directly with the founders and AI team on a small, fast-moving team — and see your work land in the product within weeks.
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