CV Engineer (EagleSight.ai) (Expression of Interest)

Posted 3 Days Ago
Be an Early Applicant
Edmonton, AB
Hybrid
Mid level
Artificial Intelligence • Machine Learning • Software
The Role
The Computer Vision Engineer will develop and optimize algorithms for detecting betting areas and chips in casinos, enhance real-time calibration, integrate dual camera feeds, and collaborate with ML engineers for data collection.
Summary Generated by Built In

NOTE: This opportunity is with EagleSight.ai building advanced surveillance and player tracking technology designed specifically for casinos.


Role Overview: The Computer Vision Engineer will work directly on the optimization of video analytics algorithms used to detect betting areas, chips, cards, and other table elements. This position will be integral to improving the reliability of object detection under various lighting and environmental conditions.

Key Responsibilities:

  • Develop and optimize computer vision algorithms for betting area and chip detection.
  • Implement improvements for real-time calibration and ROI adjustments.
  • Assist in integrating dual camera feeds and synchronizing multi-camera analytics.
  • Collaborate with ML engineers to gather data for training and validation.

Qualifications:

  • Degree in Computer Vision, Electrical Engineering, or related field.
  • 3+ years of experience in computer vision projects (OpenCV, YOLO, Aruco markers).
  • Hands-on experience with camera calibration and multi-view geometry.
  • Knowledge of C++ and Python for computer vision development.

Top Skills

C++
Python
The Company
Calgary, Alberta
107 Employees
On-site Workplace
Year Founded: 2018

What We Do

AltaML is a leading developer of AI-powered solutions. Working with organizations that want to leverage their data using artificial intelligence (AI), AltaML develops solutions that create operational efficiency, reduce risk, and generate new sources of revenue. Through a deep understanding of organizational pain points and challenges, AltaML develops solutions that encompass the entire machine learning (ML) life cycle, from evaluating potential use cases and determining feasibility, to piloting solutions, putting code into production, and ensuring models evolve over time.

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