Generative AI Engineer / Scientist (US)

Responsibilities

• Design, fine-tune, and optimize large language models (LLMs) for domain-specific applications in architecture and engineering.
• Develop agentic AI systems for reasoning, tool use, and workflow automation in structural design tasks.
• Apply graph neural networks (GNNs) to structured engineering data, knowledge graphs, and design optimization problems.
• Prototype multimodal systems combining text, drawings, and structured design data.
• Collaborate with product and engineering teams to integrate generative AI into production-ready AEC workflows.
• Stay on top of the latest AI research and propose innovative solutions to push the boundaries of generative design.

Qualifications

Education & Background

• Bachelor’s, Master’s, or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Strong academic/industrial foundation in deep learning and generative AI models, geometric learning and computational geometry.

Core Expertise

• Hands-on experience with LLMs: fine-tuning, instruction tuning, domain adaptation, or RLHF.
• Experience developing agentic systems (e.g., tool-augmented LLMs, reasoning frameworks).
• Strong knowledge of graph neural networks (GNNs) using PyTorch Geometric, DGL, or similar.
• Skilled in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX).
• Able to navigate ambiguity and adapt effectively to evolving requirements in fast-paced environments.

Preferred Qualities

• Track record of publications in top AI/ML/NLP/CV conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, KDD).
• Contributions to open-source AI projects or a demonstrable portfolio of generative AI applications.
• Experience working with multimodal or AEC-related datasets is a strong plus.
• Excited by working in a fast-growing startup environment and contributing to both research and product impact.