Applied Research Engineer

Higharc•New York, NY

1d•Remote

About The Position

Higharc is a VC-backed startup that is changing how new homes are designed and built. Join a founding team who’ve shipped products for Autodesk, Electronic Arts, Nike, and Apple. We have raised over $175M with support from top-notch venture capital firms and more than 18 strategic investors: industry leaders in construction, building products manufacturing, and distribution. Higharc is seeking a hands-on Applied Research Engineer to turn our spatial AI research into capabilities that homebuyers and builders use. You'll join our Platform R&D team, taking models that have proven themselves in research and shipping them into the product.

Requirements

5+ years of professional software development, including at least 2 years training and shipping ML models to real users
Taken at least one model end to end: data preparation, training, evaluation, serving, and the integration that put it in front of users
Fluency in Python and PyTorch (TensorFlow or Keras is fine if you're ready to move to PyTorch)
Experience with geometric or structured data such as floor plans, CAD or BIM, graphs, or 2D and 3D geometry
Built or maintained an evaluation harness or benchmark and used it to gate changes
Packaged a model behind an API (FastAPI or similar) and owned its latency and cost
A master's degree in computer science, machine learning, or a related field, or equivalent shipped work (a PhD is not required)
Comfort working remotely with a team on US Eastern hours

Nice To Haves

A background in architecture or building, whether that's a degree, professional practice, or AEC software work
Experience with constraint solvers like OR-Tools CP-SAT, optimization in general, or generative models for layouts and floor plans
Plugin or tooling work in Rhino, Grasshopper, or Revit, or experience with Hugging Face, experiment tracking, and cloud model serving on AWS or Modal, plus any publications or open source contributions

Responsibilities

Build the component layer around our layout synthesis engine: a documented API contract, a service the product can call, and an evaluation gate that runs on every change.
Turn model retraining into a one-command job, with benchmarks built in and results the whole team can read.
Own the latency and cost budgets for learned capabilities.
Extend core research into new capabilities, such as multi-story plans and real-time editing.
Partner with infrastructure engineers on MLOps and deployment, and mentor researchers and interns on engineering practices.
Share your work in writing through weekly status updates, clear pull requests, and design notes before big changes.