Senior ML Researcher

Drafted
Drafted

Software Engineering, Data Science

Posted on Sep 14, 2026
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About Drafted

Drafted is unlocking creativity in the physical world. We’re building foundational models and generative pipelines that create floor plans and renderings instantly, so anyone can imagine their dream home. Starting with single-family homes, we plan to verticalize across all dimensions of the pre-construction stack.

Generative architecture is a prime domain for applied research with abundant data, verifiable constraints, and a clear value proposition. Our team of second-time founders, engineers, and designers pairs exceptional product taste with deep technical rigor to turn real-world buildability constraints into an intuitive, magical experience.

Drafted's Values

We're a small team working fully in-person in San Francisco. We value high-ownership builders who want to be a part of a talented, highly motivated team. We're guided by the following values:

  • Own the mission. We take agency, act like owners, and see problems through to real outcomes.
  • Build in the open. We value direct feedback, fast learning, and growth through honest collaboration.
  • Move with care and speed. We iterate quickly while staying deeply respectful of our teammates.
  • Seek the why. We challenge assumptions, think from first principles, and never stop asking questions.
  • Design for everyone. We believe anyone should be able to design and build a home they love.
  • Solve what matters. We embrace hard problems and create new paths forward when none exist.

    • The Role

    As an ML researcher you’ll contribute meaningfully towards advancing the capabilities for our foundational model. You’ll work closely with teammates across both the engineering and research teams.

    Example Projects

  • Teach our ML model how to handle multiple layers of context and constraints to consider in the generation process (i.e. Structural engineering, Civil engineering, Electrical, etc).
  • Figure out how to add direct to vector as an output of the model.
  • Use RL to increase the accuracy of the model.

    • Ideal Experience

  • ⁠Iterated on novel techniques for meaningfully improving some KPI for diffusion models
  • ⁠⁠Provided code for reproducing said novel techniques if open sourced, otherwise worked with practitioners for productionizing the techniques

    • Apply Now

    Apply now
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