Applied Research Engineer

About this role

About Us 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. What You'll Do Our team builds AI that understands homes, and our generative floor plan engine has moved from proof of concept to a core workflow in Higharc 's product. You'll own the applied side of that work. The research team hands you a defined problem and a metric. You train and adapt the models, build the pipelines around them, and ship a component that meets the product's latency and cost budget. You'll work inside an architecture that already exists. The engine, the schema, and the evaluation frameworks are in place, and your job is to make them faster, reproducible, and easy for others to build on. Expect to: 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. About You You're happiest when a model you worked on is running in the product. You like taking a well-defined problem with a metric attached and closing it, and you improve an existing codebase without feeling the need to rewrite it. You think about what a 45-second wait means for a user before you think about what it means for a benchmark. You're honest about evaluation: you can tell when a result is an artifact of the test set, and you'd rather report a regression in writing than hide it behind a demo. Most of your collaborators are remote and some are part-time, so writing things down comes naturally to you. You can go from a research conversation to a product conversation to an infrastructure conversation in the same week and be understood in all three. You also know enough about buildings to see that a bedroom door opening onto a kitchen is wrong before any metric tells you. You have: 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 A major plus if you also bring: 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 expe

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