AI Research Scientist -Computer Vision

MeltPlan is building the “planning engine” for the $14 Tn construction industry, an AI system designed specifically to optimize decisions before construction begins. While design software optimizes use and aesthetics and construction software optimizes execution and control, MeltPlan is building the missing layer - software that optimizes decisions and tradeoffs upstream, before scope is locked, procurement begins, and change orders become inevitable. MeltPlan’s long-term goal is to help teams make construction “boring” by making planning more intense: surfacing constraints and tradeoffs early, aligning stakeholders before plans are frozen, and reducing the need for late-stage redlines, rework, and change orders.

MeltPlan is founded by operators who have built at scale. Kanav previously co-founded Innovaccer, a $3Bn healthtech company focused on making US healthcare more affordable and accessible. He’s now applying that systems-level thinking to construction.He’s joined by Tanmaya Kala, former Project Executive at DPR Construction, who led large commercial, healthcare, and life sciences projects. We combine deep tech scale with real construction execution.

We are looking for an AI Research Scientist – Computer Vision to enhance and manage the PlanGraph model, which transforms 2D drawings into structured graphical representations of building elements. The role involves solving downstream business use cases such as quantity takeoff, code compliance, value engineering, and constructability analysis.We are specifically looking for hands-on researchers with experience in solving real-world Computer Vision problems and building custom vision models, VLMs, or VLLMs for production-grade applications.

• Build and optimize custom Computer Vision models, VLMs, and VLLMs for construction intelligence workflows.
• Solve downstream business use cases including quantity takeoff, code compliance, value engineering, 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 closely with engineering and product teams to convert research into deployable AI systems.
• Develop benchmarking, evaluation, and validation frameworks for model performance measurement.
• Stay updated with state-of-the-art research in Computer Vision, Vision Transformers, multimodal AI, and Generative AI.
• Contribute to research documentation, patents, publications, and internal technical knowledge sharing.
• Work on real-world construction datasets, drawings, and complex edge-case problem solving.
• Drive innovation in AI systems that improve planning, decision-making, and operational efficiency in construction workflows.

• 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 working with image processing, object detection, segmentation, OCR, or multimodal AI systems.
• Understanding of Vision Language Models (VLMs), Vision Transformers (ViTs), or Generative AI concepts.
• Ability to train, fine-tune, and optimize AI models for production environments.
• Strong analytical, research, and problem-solving skills.
• 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.

Bonus if you’ve:

• Built 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.
• Worked in messy real-world data environments

We’re not looking for someone who waits for clean requirements.We’re looking for someone who thrives in the mess and turns it into systems.

• Massive industry, real-world impact
• High ownership from day one
• Small team, zero bureaucracy
• Competitive comp + meaningful equity