Computer Vision, Applied Research Scientist

Boon is the professional AI platform built specifically for construction. Founded in the San Francisco Bay Area in 2023 by product and engineering leaders from Samsara, Apple, Google and DoorDash, Boon is backed by leading Silicon Valley venture capitalists. Our AI agents embed directly into existing workflows, from preconstruction estimating to bid management. They automate the repetitive tasks that drain time and margins while surfacing the insights leaders need to make faster and more confident decisions. The result is measurable impact: teams move faster, bids are submitted sooner, win rates increase, and costs are reduced. Boon enables construction companies to build more, generate more revenue, and grow with confidence.

About the role

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 an 80/20 research-to-production role. You will propose new architectures, run the experiments that prove or disprove them, and ship the winning models to real customers. You will have autonomy over direction and experimental ideas, staying aligned with the team and the company's research focus.

What success looks like

Within your first 12-18 months, the successful candidate will:

• 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). We're committed to publishing; we may selectively not release weights or code

Responsibilities

Research & Architecture

• Design and evaluate novel multi-stage vision architectures for construction drawing understanding — perception, text-object association, and relational reasoning across elements
• 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 to ensure experiments have the data and compute they need
• Partner with engineering leadership on the accuracy roadmap and strategic direction
• Write clean internal research reports so the broader team can learn from your work
• Present findings, trade-offs, and recommendations to engineering leadership

Data & Evaluation

• Help shape what data we acquire and annotate, based on what the model actually needs
• Define evaluation datasets and metrics that track progress honestly — not Kaggle-style leaderboard chasing
• Identify failure modes on real customer drawings and design experiments that address them

Requirements

• 7+ years of computer vision research experience, ideally with a track record of published papers, open-source work, or production CV models
• Deep hands-on experience with multi-modal/vision transformers — segmentation, detection, or joint text+vision tasks
• Experience with modern vision transformer architectures like SAM, DINO, or similar foundation vision models
• Ability to move from a research idea to a trained model to a production-shipped system with minimal hand-holding
• Rigorous approach to experiments — clean baselines, meaningful ablations, honest evaluation on real data
• A point of view on architecture decisions and the ability to defend it with reasoning and experimental evidence
• Thrive on autonomy and set your own direction while staying aligned with team goals
• Clear communication in English (written and verbal) and ability to collaborate during California business hours
• 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

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 on a problem nobody in construction AI has solved — the first foundation model for construction drawings
• Autonomy to propose, defend, and run your own experiments, shipping the winners to real customers
• Publication-friendly — we support our team publishing their work at top venues
• Work at the intersection of computer vision research and real-world industry impact, in a trillion-dollar market
• Collaboration with a world-class team from top tech companies and research institutions
• Meaningful equity in an early-stage, well-funded startup

Pay

At Boon, we carefully consider a number of factors to determine starting pay, including primary work location, skills, experience, market demands, and internal parity. The actual starting annual base salary may vary based on these factors. A Boon employee may also be eligible for additional forms of compensation, such as sales incentives, discretionary bonuses, and/or equity in the company.