Computer Vision, Applied Research Scientist

The role

You will own end-to-end experiments on a foundation model for construction drawings. This includes architecture design, self-supervised pretraining, supervised fine-tuning, and shipping production models.

What you will do

• Design and evaluate novel multi-stage vision architectures
• Drive architecture decisions including backbones, decoders, and training regimes
• Run rigorous experiments with clean baselines and ablations
• Own supervised training and self-supervised pretraining strategies
• 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
• Collaborate with synthetic data, annotation, and infrastructure teams
• Partner with engineering leadership on the accuracy roadmap

Required qualifications

• 3-7+ years in computer vision research (industry research lab, applied science team, PhD research + industry, or equivalent)
• Strong track record of published CV research OR trained production CV models that shipped at scale
• Hands-on expertise in multi-modal dense prediction (segmentation, detection, or joint vision-language tasks)
• Production experience with modern vision transformer backbones (SAM, DINOv2/v3, CLIP, SigLIP, or similar)
• Strong PyTorch fluency and experience training large vision models
• Ability to move models from research to production inference pipelines
• Strong fundamentals in deep learning: optimization, loss design, regularization, self-supervised learning
• 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 (MAE, DINO, or similar)
• Experience with engineering/technical drawings, document understanding, or layout analysis
• Contributions to open-source CV research
• Published papers at top venues (CVPR, ICCV, ECCV, NeurIPS, ICLR)

Benefits

• Direct ownership of research
• Autonomy to propose, defend, and run experiments
• Support for publishing work at top venues
• Meaningful equity in an early-stage, well-funded startup
• Sales incentives, discretionary bonuses, and/or equity

About the company

Boon is the professional AI platform built specifically for construction. Founded in the San Francisco Bay Area in 2023, it features AI agents that automate repetitive tasks in construction workflows.