Edge Engineer

Join a small, high-impact engineering team building AI-powered computer vision infrastructure that's already deployed and running in live retail environments. This is a great opportunity to own meaningful, technically challenging work on the cutting edge of edge engineering, computer vision, and real-time business intelligence.
Take end-to-end ownership of our client’s edge stack, supporting the full on-device pipeline powering computer vision AI systems on high traffic live camera feeds.
You'd own a real system that is already in customers' stores, with clear and unsolved technical problems in front of you.
You'll be part of high impact team as our client enters an exciting period of growth. Your contributions matter immediately, and you will shape their technical direction.
What & Why:A fast-growing retail AI company is looking for an Edge Engineer to own the on-device computer vision pipeline powering its in-store analytics platform. This hire is being made to deepen engineering capacity ahead of a significant multi-site expansion.
This position is hybrid 2 days on-site in Vancouver.
Who:Our client is a small but proven technology company operating in the retail intelligence space, building edge AI infrastructure that runs entirely in-store on constrained hardware.
The team has a startup culture at its core — people here wear multiple hats, take ownership seriously, and move fast without sacrificing rigor.
You:You will bring the following education, skills and experience to the role:
2+ years of hands-on experience deploying computer vision or inference workloads to production edge hardware (NVIDIA Jetson or comparable embedded/accelerator platforms strongly preferred)
Solid Python skills, including comfort with performance-sensitive, production-quality code
Practical experience with a production inference runtime such as TensorRT, ONNX Runtime, OpenVINO, or TFLite
Working knowledge of multi-object tracking — association algorithms, IoU matching, Kalman filtering, and real-world failure modes like occlusion and crowding
Demonstrated rigor around evaluation: you've owned a benchmark harness, managed labeled datasets, or built an accuracy regression process, and you understand that measurement quality matters as much as model quality
Startup mindset — comfortable owning problems, working with limited resources, and contributing beyond a narrow job description
Compensation:$120,000 to $150,000.
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