Generative AI Engineer (Intern) - US

Responsibilities

• Assist in fine-tuning and evaluating Large Language Models (LLMs) for architecture and engineering scenarios.
• Participate in the research and development of prototype AI agent systems for reasoning, process automation, and tool augmentation.
• Support experiments with Graph Neural Networks (GNNs) on structured engineering and design data.
• Explore and implement image generation methods (e.g., diffusion models, GANs) for drawing synthesis and optimization.
• Assist in building multi-modal processing pipelines that integrate text, drawings, and structured data.
• Document experimental processes, write benchmark results, and report findings to the team.

Qualifications

Education & Background: Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Have coursework or project experience in Machine Learning, Computer Vision, or Natural Language Processing.

Core Expertise: Familiarity with Large Language Models (HuggingFace Transformers, fine-tuning, Prompt Engineering). Exposure to Graph Neural Networks (PyTorchGeometric, DGL) in courses or projects. Basic understanding of image generation models (GANs, diffusion models, etc.). Proficiency in Python and at least one major ML framework (PyTorch, TensorFlow, or JAX).

Preferred Qualities: Experience in AI/ML-related research, publications, or open-source contributions is a plus. Curiosity about the applications of AI in the architecture, engineering, and construction (AEC) industry. Self-motivated, eager to learn, and able to adapt to a fast-paced startup environment.

Why Join Us?

• You will directly participate in the R&D of the next-generation GenAI Copilot for Structural Engineers, with the opportunity for a full-time offer for outstanding performers.
• Gain in-depth, hands-on experience with LLM fine-tuning, agent systems, GNNs, and image generation models.
• Learn how to apply generative AI to real-world problems in the Architecture, Engineering, and Construction (AEC) industry.
• Learn best practices for data preparation, training pipelines, and model benchmarking.
• Gain front-line, practical experience at a startup, not just a "supporting