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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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