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Founding ML Engineer
Draftaid
Toronto · Hybrid Full-time 2w ago
About the role
About
DraftAid is building the intelligence layer for mechanical engineering. We started by auto-generating manufacturing drawings from 3D CAD parts, and we're building the representations to go much further.
Responsibilities
- 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
Requirements
- 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
Benefits
- Flexible hours and hybrid in-office
- Competitive salary and equity package.
- Small team, high ownership
Requirements
- 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
Responsibilities
- 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.
- Own problems, not tickets
Benefits
Flexible hourshybrid in-office
Skills
C#LLMsTypeScript
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