As a Computer Vision Engineer with 🦅
EagleSight.ai
, you will architect, build, and deploy state‑of‑the‑art vision algorithms that power rapid, per‑property rollouts of our solution. This hands‑on role blends research, engineering, and real‑world deployment: you’ll turn novel ideas into robust, optimized models running at scale on edge and cloud platforms.
This role is based out of Edmonton, Alberta with a requirement to be in office 3+ days per week. Only those who are qualified and reside in Edmonton will be contacted.
What You'll Do:
- Design & Implement – Develop and productionize computer vision solutions (e.g. object detection, segmentation, tracking) tailored for property‑level deployments
- Optimize & Accelerate – Apply model compression, quantization, and hardware‑specific optimizations (TensorRT, ONNX Runtime, etc.) to achieve low latency and high throughput on edge devices and on-prem servers.
- Build End‑to‑End Pipelines – Create data ingestion, annotation, training, validation, and CI/CD workflows that seamlessly integrate with our Project Intercept platform
- Collaborate Cross‑Functionally – Partner with ML Developers, Software Engineers (frontend & backend), Product Managers, and Designers to bring features from prototype to production
- Deploy & Monitor – Containerize models (Docker, Kubernetes) for automated deployment, then instrument performance monitoring and feedback loops to continually improve accuracy and reliability
- Code Review & Mentorship – Maintain high engineering standards through peer reviews and knowledge sharing, supporting the growth of our vision‑AI practice
- Stay on the Cutting Edge – Research emerging computer vision techniques and recommend the best fit for our product roadmap
What You Bring:
- 3+ years of experience developing computer vision algorithms Strong CV Foundations – Hands‑on experience with OpenCV and deep learning frameworks such as TensorFlow, PyTorch, or Caffe
- Programming Expertise – Proficient in Python; C++ experience an asset for performance‑critical components
- Model Optimization Skills – Familiarity with quantization, pruning, and hardware accelerators (GPU, VPU)
- Real‑Time Processing Know‑How – Experience building vision pipelines for live video streams or embedded systems
- API & Microservices – Solid understanding of RESTful APIs, microservice architectures, and CI/CD tooling (e.g., GitHub Actions)
- Entrepreneurial Mindset – Thrive in a fast‑paced environment, ready to iterate quickly and own end‑to‑end delivery
Bonus Points:
- Familiarity with edge‑deployment SDKs (e.g., NVIDIA Jetson)
- Experience with GPU accelerated frame capture/processing pipelines such as GStreamer/NVIDIA DeepStream SDK
- Background in container orchestration or infrastructure as code using Ansible
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