Computer Vision, Applied Research Scientist

Role Description

We are building the first foundation model for construction drawings — a unified multi-modal vision system that reads, understands, and reasons about architectural, mechanical, electrical, plumbing, and structural plans the way a human estimator does.

As a Computer Vision Applied Research Scientist at Boon, you will own end-to-end experiments on our foundation model, from architecture design through self-supervised pretraining, supervised fine-tuning, and shipping production models into our inference pipeline. This is a 80/20 research-to-production role.

• Propose new architectures, run experiments, and ship winning models to real customers.
• Have autonomy over direction and experimental ideas, staying aligned with the team and the company's research focus.

What Success Looks Like

• Push our production model to ≥95% accuracy across multiple trades and scopes.
• Design a genre-defining, novel architecture for construction drawing understanding.
• Publish a paper on the work at a top venue (CVPR, ICCV, ECCV, NeurIPS, or ICLR).

What You Will Do

• Research & Architecture
  • Design and evaluate novel multi-stage vision architectures for construction drawing understanding.
  • Drive architecture decisions: backbones, decoders, fusion strategies, loss functions, training regimes.
  • Run rigorous experiments with clean baselines, ablations, and held-out evaluation on real construction drawings.
  • Own supervised training and self-supervised pretraining strategies.
  • Pursue research directions that compound accuracy across trades and scopes.
• Production Shipping
  • Take models from experimental notebooks to the production inference pipeline.
  • Work hands-on with PyTorch, YOLO, SAM, DINO, and other modern CV stacks.
  • Collaborate with ML engineers on deployment, quantization, and serving.
  • Debug real failures on real customer drawings and close the loop into the next training run.
• Cross-Functional Work
  • Collaborate with the synthetic data, annotation, and infrastructure teams.
  • Partner with engineering leadership on the accuracy roadmap and strategic direction.
  • Write clean internal research reports.
  • Present findings, trade-offs, and recommendations to engineering leadership.
• Data & Evaluation
  • Help shape what data we acquire and annotate.
  • Define evaluation datasets and metrics that track progress honestly.
  • Identify failure modes on real customer drawings and design experiments that address them.

Qualifications

• 3-7+ years of computer vision research experience.
• Track record of published papers, open-source work, or production CV models.
• Deep hands-on experience with multi-modal/vision transformers.
• Experience with modern vision transformer architectures like SAM, DINO, or similar.
• Ability to move from a research idea to a trained model to a production-shipped system.
• Strong fundamentals in deep learning: optimization, loss design, regularization, self-supervised learning.
• Fluency in English (written and verbal).

Requirements

• 3-7+ years in computer vision research.
• Strong track record of published CV research OR trained production CV models that shipped at scale.
• Hands-on expertise in multi-modal dense prediction.
• Production experience with modern vision transformer backbones.
• Strong PyTorch fluency and experience training large vision models.
• Ability to move models from research to production inference pipelines.
• Fluency in English (written and verbal).

Nice to Have

• Experience with Graph Neural Networks or relational reasoning architectures.
• Experience with text spotting, OCR, or scene text detection integrated with vision models.
• Experience with LoRA, adapters, or parameter-efficient fine-tuning of large vision models.
• Experience with self-supervised pretraining.
• Experience with engineering/technical drawings, document understanding, or layout analysis.
• Contributions to open-source CV research.
• Published papers at top venues.

Benefits

• Direct ownership of research on a problem nobody in construction AI has solved.
• Autonomy to propose, defend, and run your own experiments.
• Publication-friendly environment.
• Work at the intersection of computer vision research and real-world industry impact.
• Collaboration with a world-class team.
• Meaningful equity in an early-stage, well-funded startup.

Compensation

At Boon, we want to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contribution to the company. The actual starting annual base salary may vary based on several factors.

Company Description

Boon is the professional AI platform built specifically for construction, founded in the San Francisco Bay Area in 2023. Our AI agents automate repetitive tasks and surface insights for faster decision-making in construction.