Senior Computer Vision Engineer- US

STACK Construction Technologies•Cincinnati, OH

23d•$190,000 - $210,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.