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
Market-market-competitive salary and equity package
Small team, high ownership
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📌 Founding Ml Engineer Toronto
🏢 Draftaid
📍 Toronto