AI/ML Researcher - Intern

Hedral Inc. • New York, NY

2d • Hybrid

About The Position

Hedral is redefining building design through automated, multi-disciplinary engineering. We develop and operate a proprietary platform that accelerates design schedules, enabling us to deliver the output of traditional AEC firms ten times our size. We are fully licensed to stamp these designs as Engineer of Record and take responsibility for highly-consequential projects, from mission-critical federal facilities to complex commercial infrastructure. We are scaling quickly with backing from leading venture capital including Khosla Ventures, Valor Equity Partners, and Tishman Speyer.

We are seeking a Machine Learning Intern to join our innovative team. This internship will focus on applying advanced computer vision, language models, and reinforcement learning to complex, domain-specific architectural and engineering data. The role offers the unique opportunity to work on cutting-edge problems at the intersection of AI, spatial reasoning, and the AEC industry, helping shape the future of automated design and structural analysis.

Requirements

• 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 TensorFlow.
• 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.

Nice To Haves

• Experience with any of vision, language, and graph neural networks or 3D/geometric deep learning, including CNNs (U-Net, ResNet), 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., SAP2000, 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.

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.

Benefits

• Competitive Compensation and Benefits.
• Environment for growth.
• Engineering Redefined: Hands-on experience with the future of automated design that goes beyond traditional manual workflows.
• Mentorship & Velocity: Fast-track career development working alongside industry leaders in both structural engineering and software development.

What This Job Offers

• Career Level: Intern
• Education Level: No Education Listed