Overview

DraftAid is building the intelligence layer for mechanical engineering. We started by auto-generating manufacturing drawings from 3D CAD parts. We are now building representations that enable us to go much further.

What you'll do

- Design learned representations over a large corpus of 3D assemblies and their associated manufacturing drawings
- Train and evaluate models that drive drawing generation decisions
- Build the data and training infrastructure from scratch: pipelines, eval harnesses, dataset curation
- Integrate models into a production geometry engine written in C#
- Own the full ML stack. There is no existing ML team; you are it
- Own problems, not tickets

What we're looking for

- Deep experience training encoder‑decoder architectures and representation learning systems from scratch




- Practical experience building with LLMs as components in larger systems
- Comfort working with 3D data: meshes, B‑rep, point clouds, or similar geometric representations
- The ability to look at a messy, domain‑specific corpus and figure out what signal is in it

Nice to have

- Experience with 3D world models and spatial reasoning systems
- Background in robotics perception, 3D reconstruction, NeRFs, or geometric deep learning
- Familiarity with C# or TypeScript

What we offer

- Flexible schedule and hybrid in-office
- Competitive salary and equity package
- Small team, high ownership

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📌 Founding ML Engineer (Toronto)
🏢 Draftaid
📍 Toronto