Research Engineer

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

What You'll Do

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

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