AI Research Scientist - Computer Vision

About the Company

MeltPlan is building the “planning engine” for the $14 Tn construction industry, an AI system designed specifically to optimize decisions before construction begins. MeltPlan’s long-term goal is to help teams make construction “boring” by surfacing constraints and tradeoffs early and reducing the need for late-stage rework.

Responsibilities

• Build and optimize custom Computer Vision models, VLMs, and VLLMs for construction intelligence workflows
• Solve downstream business use cases including quantity takeoff, code compliance, and constructability analysis
• Conduct applied research in Computer Vision, multimodal AI, document understanding, and spatial reasoning
• Design, train, fine-tune, and evaluate production-grade AI models using large-scale datasets
• Improve model accuracy, robustness, inference performance, and scalability for real-world deployment
• Collaborate with engineering and product teams to convert research into deployable AI systems
• Develop benchmarking, evaluation, and validation frameworks for model performance
• Stay updated with state-of-the-art research in Vision Transformers and Generative AI

Requirements

• Strong foundation in Computer Vision, Deep Learning, and Machine Learning
• Hands-on experience building and training custom Computer Vision models
• Proficiency in Python and frameworks such as PyTorch, TensorFlow, and OpenCV
• Experience with image processing, object detection, segmentation, OCR, or multimodal AI systems
• Understanding of Vision Language Models (VLMs), Vision Transformers (ViTs), or Generative AI
• Ability to train, fine-tune, and optimize AI models for production environments
• Experience reading and implementing research papers into practical applications
• Bachelor’s, Master’s, or PhD in Computer Science, AI, Machine Learning, or a related field

Preferred Qualifications

• Experience building multi-step agent systems
• Prior experience in applied AI research within startup or product-focused environments
• Exposure to MLOps, model deployment, and production AI systems
• Experience working with messy real-world data environments