{"id":821202,"url":"https://alion.io/job/getboon-computer-vision-applied-research-scientist","title":"Computer Vision, Applied Research Scientist","company":{"id":670453,"name":"Boon","domain":"getboon.ai","url":"https://alion.io/company/getboon","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":180000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CLIP","optional":false},{"name":"Computer Vision","optional":false},{"name":"DINOv2","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Vision Transformer","optional":false},{"name":"YOLO","optional":false},{"name":"LoRA","optional":true},{"name":"OCR","optional":true},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-08-12T13:43:59Z","employer_posted_date":"2026-08-12","last_verified_at":"2026-11-05T14:03:55Z","board_verified":true,"closed_at":null,"days_open":85,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":85},"description":"About Boon\nBoon 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.\nOur 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.\nThe 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.\nAbout the Role\nWe 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.\nAs 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.\nThis 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.\nWhat Success Looks Like\nWithin your first 12-18 months, the successful candidate will:\nPush our production model to ≥95% accuracy across multiple trades and scopes\n\nDesign a genre-defining, novel architecture for construction drawing understanding\n\nPublish 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\n\nWhat You Will Do\nResearch & Architecture\nDesign and evaluate novel multi-stage vision architectures for construction drawing understanding - perception, text-object association, and relational reasoning across elements\n\nDrive architecture decisions: backbones, decoders, fusion strategies, loss functions, training regimes\n\nRun rigorous experiments with clean baselines, ablations, and held-out evaluation on real construction drawings\n\nOwn supervised training and self-supervised pretraining strategies\n\nPursue research directions that compound accuracy across trades and scopes\n\nProduction Shipping\nTake models from experimental notebooks to the production inference pipeline\n\nWork hands-on with PyTorch, YOLO, SAM, DINO, and other modern CV stacks\n\nCollaborate with ML engineers on deployment, quantization, and serving\n\nDebug real failures on real customer drawings and close the loop into the next training run\n\nCross-Functional Work\nCollaborate with the synthetic data, annotation, and infrastructure teams to make sure experiments have the data and compute they need\n\nPartner with engineering leadership on the accuracy roadmap and strategic direction\n\nWrite clean internal research reports so the broader team can learn from your work\n\nPresent findings, trade-offs, and recommendations to engineering leadership\n\nData & Evaluation\nHelp shape what data we acquire and annotate, based on what the model actually needs\n\nDefine evaluation datasets and metrics that track progress honestly - not Kaggle-style leaderboard chasing\n\nIdentify failure modes on real customer drawings and design experiments that address them\n\nYou Are a Great Fit If\nYou have 7+ years of computer vision research experience, ideally with a track record of published papers, open-source work, or production CV models\n\nYou have deep hands-on experience with multi-modal/vision transformers - segmentation, detection, or joint text+vision tasks\n\nYou have worked with modern vision transformer architectures like SAM, DINO, or similar foundation vision models\n\nYou can move from a research idea to a trained model to a production-shipped system with minimal hand-holding\n\nYou think about experiments rigorously - clean baselines, meaningful ablations, honest evaluation on real data\n\nYou have a point of view on architecture decisions and can defend it with reasoning and experimental evidence\n\nYou thrive on autonomy and set your own direction while staying aligned with team goals\n\nYou communicate clearly in English (written and verbal) and can collaborate during California business hours\n\nRequirements\n3-7+ years in computer vision research (industry research lab, applied science team, PhD research + industry, or equivalent)\n\nStrong track record of published CV research OR trained production CV models that shipped at scale\n\nHands-on expertise in multi-modal dense prediction (segmentation, detection, or joint vision-language tasks)\n\nProduction experience with modern vision transformer backbones (SAM, DINOv2/v3, CLIP, SigLIP, or similar)\n\nStrong PyTorch fluency and experience training large vision models\n\nAbility to move models from research to production inference pipelines\n\nStrong fundamentals in deep learning: optimization, loss design, regularization, self-supervised learning\n\nFluency in English (written and verbal)\n\nNice to Have\nExperience with Graph Neural Networks or relational reasoning architectures (we do not expect this - most CV researchers do not - but it is a meaningful plus)\n\nExperience with text spotting, OCR, or scene text detection integrated with vision models\n\nExperience with LoRA, adapters, or parameter-efficient fine-tuning of large vision models\n\nExperience with self-supervised pretraining (MAE, DINO, or similar)\n\nExperience with engineering/technical drawings, document understanding, or layout analysis\n\nContributions to open-source CV research\n\nPublished papers at top venues (CVPR, ICCV, ECCV, NeurIPS, ICLR)\n\nWhat We Offer\nDirect ownership of research on a problem nobody in construction AI has solved - the first foundation model for construction drawings\n\nAutonomy to propose, defend, and run your own experiments, shipping the winners to real customers\n\nPublication-friendly - we support our team publishing their work at top venues\n\nWork at the intersection of computer vision research and real-world industry impact, in a trillion-dollar market\n\nCollaboration with a world-class team from top tech companies and research institutions\n\nMeaningful equity in an early-stage, well-funded startup\n\nCompensation\nAt 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. With that in mind, we carefully consider a number of factors to determine the appropriate starting pay for an employee, including their primary work location and an assessment of a candidate’s skills and experience, as well as market demands and internal parity. This estimate can vary based on the above mentioned factors, so the actual starting annual base salary may be above or below this range. A Boon employee may be eligible for additional forms of compensation, depending on their role, including sales incentives, discretionary bonuses, and/or equity in the company.\nBoon Is An Equal Opportunity Employer\nAs an equal-opportunity employer, Boon is committed to providing employment opportunities to all individuals. 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