# Computer Vision, Applied Research Scientist at Boon > Remote · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aboon%3A2570bca0-033a-4c72-8ad9-d45227bfdde7 (read 2026-11-13). ## Facts (observed) - Company: [Boon](https://alderhq.app/companies/boon) - Location text: United States (Remote) - Where it can be done from (inferred): Remote · United States (evidence: "Location: United States (Remote)") - Posted: 2026-08-12 (93 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-10 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/boon/2570bca0-033a-4c72-8ad9-d45227bfdde7 - Skills listed (inferred): PyTorch ## Pay and sponsorship - Posted pay: $180k–$250k per year (from the listing's pay field) - Market band for similar posted roles (United States, n 914): p25 $183k, median $223k, p75 $275k ## Description (the employer's text) ### About Boon 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 a 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. This is not a role for someone who wants to be told what to build. ### 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 ### What You Will Do ### 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 make sure 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 ### You Are a Great Fit If You have 7+ years of computer vision research experience, ideally with a track record of published papers, open-source work, or production CV models You have deep hands-on experience with multi-modal/vision transformers — segmentation, detection, or joint text+vision tasks You have worked with modern vision transformer architectures like SAM, DINO, or similar foundation vision models --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aboon%3A2570bca0-033a-4c72-8ad9-d45227bfdde7 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.