stackct

Senior Computer Vision Engineer- Canada

Toronto, Ontario, Canada · Senior

Work authorization required$250k-$280kDetected 15 hours ago

Computer VisionRadiologyResearch

About the role

This role focuses on detection and segmentation quality, geometric accuracy, and production-grade pipelines that work reliably with messy real-world PDFs and drawings.
Increase model accuracy, boundary and geometric quality, and overall reliability of predictions for downstream use.

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.
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.
Take thoughtful action, remain accountable, and own both successes and mistakes.
Share knowledge, collaborate across teams, and invest in one another's development.
We are committed to building a team that represents a variety of backgrounds, perspectives, and skills.

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.
Backend engineering experience, including APIs, async processing, and scalable GPU services.
5+ years experience building computer vision systems: detection, segmentation, or structured geometry extraction, used high volume in production.
Experience with multimodal models (vision-language models, document AI systems)
Understanding of grounding - linking model outputs to source data or coordinates
Additional Preferred Qualifications:
Background in document-heavy CV domains such as construction, real estate, medical imaging, geospatial, or similar workflows.

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.
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.
Clear quality metrics and a repeatable improvement loop.
What This Role is Not:

We take into account an individual's qualifications, skillset, and experience in determining final salary.

We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
This role is eligible for health insurance (medical, dental & vision), life insurance, 401(k) & paid time off.
We are hiring a Senior Computer Vision Engineer to improve our computer vision system for construction documents.

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.
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.

This listing is sourced directly from stackct's careers page and normalized into a canonical job model.