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 hiring a Research Engineer to join our Special Projects team. In this role, you'll conduct foundational research in spatial AI for residential construction — developing novel approaches at the intersection of reinforcement learning, computer vision, LLMs, and 3D geometry to solve problems that have no off-the-shelf solutions.

What You'll Do

You'll produce the research capabilities that power Higharc's AI platform, working on problems that don't exist anywhere else. Expect to:

Design and conduct original research in spatial reasoning for residential construction, developing models that understand architectural structure, spatial relationships between building components, and the geometric constraints that govern how homes can be built.

Design end-to-end LLM training and fine-tuning pipelines tailored to construction domains, developing domain-specific pre-training and SFT datasets from architectural drawings, building codes, product specifications, and construction documentation.

Apply computer vision techniques to extract semantic structure from architectural inputs and develop multimodal training pipelines that link visual and textual representations of building data, enabling models that reason across drawings, specifications, and code requirements simultaneously.

Develop RL-based approaches for generative architectural design and architect RAG systems that ground LLM outputs in authoritative construction knowledge bases, reducing hallucination for high-stakes building decisions.

Define construction-domain benchmarks, build ground-truth datasets, and establish eval pipelines that measure model performance against real homebuilding tasks rather than generic benchmarks. Research emerging foundation models and parameter-efficient fine-tuning approaches (LoRA, QLoRA, adapter layers) to inform Higharc's model strategy.

Deliver research outputs ready for integration, including documented model artifacts, serving-ready pipelines, and clear API contracts, and partner with the Prototyping Engineer to deploy fine-tuned models and RAG systems into the Higharc platform.

About You

You do your best work when you can go deep. You treat residential construction as a worthy and complex problem space, not a stepping stone to a more prestigious domain, and you bring the same intellectual honesty to negative results as to positive ones. You understand that research that never ships is indistinguishable from research that never happened.

You have:

5+ years of professional software engineering experience, with 2+ years directly on LLM development, fine-tuning, or applied ML systems in production

Hands-on experience with model training frameworks (HuggingFace Transformers, PyTorch) and fine-tuning workflows on domain-specific corpora

Practical experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector), and embedding model selection

Experience with 3D modeling, computational geometry, or computer graphics in a research or production context

Strong proficiency in Python across the ML ecosystem; React/TypeScript proficiency for integration work

A Master's degree in Computer Science, Machine Learning, AI, or a closely related field

A major plus if you also bring:

A Ph.D., particularly for candidates focused on model research and pre-training

Open-source contributions or published research

Familiarity with BIM tools (Revit, AutoCAD) or BIM data formats (IFC, gbXML)

Experience with Three.js or equivalent 3D frameworks

While we've identified the core experience and skills required above, please still apply if you have more or different experience than this! We will use your previous experience and performance across the series of interviews to establish appropriate level within our organization in a fair and equitable way.