AI/ML Researcher – Intern

Hedral

Machine Learning Intern applying advanced computer vision and language models to engineering and architectural data. Opportunity to work on cutting-edge AI problems in the AEC industry.

Posted 6/24/2026internshipNew York City • New York, Texas • 🇺🇸 United StatesEntry LevelWebsite

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Pythondeep learningmachine learning algorithmsobject detectionsemantic segmentationreinforcement learningsupervised learningunsupervised learningmodel trainingmodel evaluation

Soft Skills

collaborationresearch experiencecritical thinkingproblem-solving

Tools & Technologies

PyTorchTensorFlowMLOpsSAP2000ETABSRevitCADBIM

Industry Keywords

AECarchitectural plansengineering documentsbuilding system data3D geometriesspatial representationsreinforced concrete designbuilding codesASCE 7ACI 318

Tech Stack

Tools & technologies

PythonPyTorchTensorflow

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.
Bonus Qualifications: 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.

Benefits

Comp & perks

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.