Computer Vision, Applied Research Scientist

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Boon

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2023

📦 Logistics

💼 Consulting

🤖 Artificial Intelligence

Logistics • Consulting • Artificial Intelligence

Boon is an AI platform designed for commercial fleets, focusing on automating critical processes such as data entry, load sourcing, compliance, and document management. With recent funding of $20. 5M, Boon aims to streamline operations for companies by connecting existing systems and minimizing manual workflows. The platform employs AI agents to handle repetitive tasks, enhance communication between drivers and dispatchers, and provide real-time alerts, ultimately leading to increased efficiency and reduced human error.

📋 Description

• Own end-to-end experiments on Boon’s foundation model, from architecture design through self-supervised pretraining, supervised fine-tuning, and production deployment • Design and evaluate novel multi-stage vision architectures for construction drawing understanding • Drive decisions on backbones, decoders, fusion strategies, loss functions, and training regimes • Run rigorous experiments with baselines, ablations, and held-out evaluation on real construction drawings • Pursue research directions to improve accuracy across construction trades and scopes • Move models from experimental notebooks into the production inference pipeline • Work hands-on with PyTorch, YOLO, SAM, DINO, and other modern computer-vision stacks • Collaborate with ML engineers on deployment, quantization, and serving • Debug failures on customer drawings and incorporate findings into subsequent training runs • Collaborate with synthetic-data, annotation, and infrastructure teams on data and compute needs • Partner with engineering leadership on the accuracy roadmap and strategic direction • Write internal research reports and present findings, trade-offs, and recommendations to engineering leadership • Help determine data acquisition and annotation priorities • Define evaluation datasets and metrics • Identify failure modes and design experiments to address them

🎯 Requirements

• 3–7+ years in computer vision research • Strong track record of published computer-vision research or trained production computer-vision models shipped at scale • Hands-on expertise in multi-modal dense prediction, including segmentation, detection, or joint vision-language tasks • Production experience with modern vision-transformer backbones such as SAM, DINOv2/v3, CLIP, SigLIP, or similar • Strong PyTorch fluency and experience training large vision models • Ability to move models from research into production inference pipelines • Strong deep-learning fundamentals, including optimization, loss design, regularization, and self-supervised learning • Fluency in English, written and verbal • Ability to collaborate during California business hours • Experience with Graph Neural Networks or relational reasoning architectures is a plus • Experience with text spotting, OCR, or scene-text detection is a plus • Experience with LoRA, adapters, or parameter-efficient fine-tuning is a plus • Experience with self-supervised pretraining is a plus • Experience with engineering or technical drawings, document understanding, or layout analysis is a plus • Contributions to open-source computer-vision research are a plus • Published papers at top venues are a plus

🏖️ Benefits

• Meaningful equity in an early-stage, well-funded startup • Publication-friendly — we support our team publishing their work at top venues • Autonomy to propose, defend, and run your own experiments, shipping the winners to real customers • Collaboration with a world-class team from top tech companies and research institutions • Work at the intersection of computer vision research and real-world industry impact, in a trillion-dollar market • Potential additional compensation including discretionary bonuses and/or equity in the company