Senior Computer Vision Engineer

About the opportunity

About Us: STACK is a leading provider of cloud-based construction estimating and takeoff software solutions, committed to helping businesses transform through innovative solutions. We pride ourselves on fostering a collaborative, dynamic environment where team members have the opportunity to grow and make a real impact. About the Role: We are hiring a Senior Computer Vision Engineer to improve our computer vision system for construction documents. This role focuses on detection and segmentation quality, geometric accuracy, and production-grade pipelines that work reliably with messy real-world PDFs and drawings. What You’ll Do: Design, build, and improve end-to-end detection and segmentation pipelines for document images. Improve document ingestion for PDFs and other unstructured files, including parsing, rendering, and handling of multi-page and multi-view content. Increase model accuracy, boundary and geometric quality, and overall reliability of predictions for downstream use. Integrate modern detection and segmentation models into production workflows and build the post-processing that turns model output into structured, usable geometry. Define evaluation metrics, investigate failure cases, and drive continuous quality improvements. Optimize latency, reliability, and cost across the inference and post-processing stack. Own training infrastructure, dataset curation, annotation quality, and continuous-improvement loops. Make architectural decisions and own system quality end to end. What You Bring: 5+ years experience building computer vision systems: detection, segmentation, or structured geometry extraction, used high volume in production. Experience working with messy, real-world image data or large unstructured visual datasets. Strong understanding of detection and segmentation tradeoffs, including model architecture choices, training data design, and post-processing. Ability to measure system performance with evaluation, testing, and production metrics. Ability to explain failure modes clearly and improve systems through debugging, dataset work, and iteration. Experience with multimodal models (vision-language models, document AI systems) Understanding of grounding — linking model outputs to source data or coordinates Backend engineering experience, including APIs, async processing, and scalable GPU services. Additional Preferred Qualifications: Experience with polygon or mask post-processing, geometric regularization, or CAD-style structured output. Experience with layout-aware document processing, PDF vector extraction, or combining raster and vector signals. Background in document-heavy CV domains such as construction, real estate, medical imaging, geospatial, or similar workflows. Experience optimizing inference cost and latency at scale. Familiarity with open-source detection / segmentation ecosystems, training infrastructure, or model serving. What Success Looks Like: Accurate, geometrically correct predictions suitable for downstream measurement, and analysis use. Fast, reliable inference across large and messy real-world document sets. Clear quality metrics and a repeatable improvement loop. Systems that perform consistently under real-world production constraints. What This Role is Not: Not a model-training-only role. You'll own data, training, post-processing, and serving. Not a research-only role. Not a plug-and-play CV tools environment. Why Join STACK? Opportunity to work in a fast-paced, growth-oriented, remote-first environment. Async-friendly environment with a focus on ownership and deep work. Be part of a dynamic, supportive team where your contributions are valued. At STACK, our values shape how we work, collaborate, and serve our customers: Radical Honesty: Communicate directly, respectfully, and transparently—even when conversations are difficult. Give and receive feedback with care. Customer Obsession: Put the customer at the center of every decision. Solve real problems, follow through, and measure success by customer outcomes. Move With Purpose: Take thoughtful action, remain accountable, and own both successes and mistakes. Anticipate what’s next and adapt as you learn. Grow Together: Share knowledge, collaborate across teams, and invest in one another’s development. Celebrate collective wins and treat setbacks as opportunities to learn. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our work will be. We are interested in every qualified candidate who is eligible to work in the United States. However, we are not able to sponsor visas. We take into account an individual’s qualifications, skillset, and experience in determining final salary. This role is eligible for health insurance (medical, dental & vision), life insurance, 401(k) & paid time off. The expected compensation range for this position is about $250k-280k CAD. The actual offer will be at the company’s sole discretion and determined by relevant business considerations, including the final candidate’s qualifications, years of experience, skillset, and geographic location. #LI-Remote

Responsibilities

• : Design, build, and improve end-to-end detection and segmentation pipelines for document images.
• Improve document ingestion for PDFs and other unstructured files, including parsing, rendering, and handling of multi-page and multi-view content.
• Increase model accuracy, boundary and geometric quality, and overall reliability of predictions for downstream use.
• Integrate modern detection and segmentation models into production workflows and build the post-processing that turns model output into structured, usable geometry.
• Define evaluation metrics, investigate failure cases, and drive continuous quality improvements.
• Optimize latency, reliability, and cost across the inference and post-processing stack.
• Own training infrastructure, dataset curation, annotation quality, and continuous-improvement loops.
• Make architectural decisions and own system quality end to end.

Qualifications

• : 5+ years experience building computer vision systems: detection, segmentation, or structured geometry extraction, used high volume in production.
• Experience working with messy, real-world image data or large unstructured visual datasets.
• Strong understanding of detection and segmentation tradeoffs, including model architecture choices, training data design, and post-processing.
• Ability to measure system performance with evaluation, testing, and production metrics.
• Ability to explain failure modes clearly and improve systems through debugging, dataset work, and iteration.
• Experience with multimodal models (vision-language models, document AI systems) Understanding of grounding — linking model outputs to source data or coordinates Backend engineering experience, including APIs, async processing, and scalable GPU services.
• : Experience with polygon or mask post-processing, geometric regularization, or CAD-style structured output.
• Experience with layout-aware document processing, PDF vector extraction, or combining raster and vector signals.