AI​/ML Researcher – Intern

Responsibilities

• Research and prototype computer vision models and language models tailored for domain-specific tasks, such as understanding and processing architectural plans, engineering documents, and building system data.
• Develop robust data pipelines for curating, training, and fine-tuning models on diverse engineering data, including 2D drawings, 3D geometries, and text-based specifications.
• Implement machine learning algorithms for tasks such as object detection, semantic segmentation, and advanced reasoning within the AEC domain.
• Explore and implement reinforcement learning frameworks to optimize and automate complex decision‑making processes in the built environment.
• Collaborate with the engineering team to deploy AI models into our core design and analysis workflows, applying MLOps best practices for scalable machine learning deployment.
• Conduct rigorous experiments and evaluate model performance on real‑world AEC use cases to ensure scalability and accuracy.

• Currently enrolled in a graduate or undergraduate program in Computer Science, Engineering, Machine Learning, Applied Mathematics, or a related field.
• Strong proficiency in Python, with a solid foundation in deep learning and hands‑on experience using frameworks like PyTorch or Tensor Flow.
• Familiarity with core machine learning concepts and techniques, including supervised/unsupervised learning, model training and evaluation, and common architectures (e.g., CNNs, GNNs, transformers).
• Demonstrated research experience, such as publications, preprints, conference submissions, or substantive research projects, with the ability to read, critique, and build on recent ML literature.
• Bonus
  
  Qualifications:
  
  Experience with any of vision, language, and graph neural networks or 3D/geometric deep learning, including CNNs (U‑Net, Res Net), GNNs, and NeRFs.
• Familiarity with modern vision, language generative models (e.g., VAEs, Diffusion, Transformers, ViTs, Multimodal models).
• Knowledge of reinforcement learning principles and frameworks as applied to optimization or decision‑making problems.
• Background in Engineering, Architecture, or AEC with hands‑on experience processing complex engineering data or spatial representations (CAD/BIM), and familiarity with relevant software (e.g., SAP
  2000, ETABS, Revit), reinforced concrete/steel design, and building codes (e.g., ASCE 7, ACI 318, AISC 360).
• Experience with MLOps practices, including experiment tracking, model deployment, or working with large‑scale datasets and distributed training.