Edge Engineer

Swim Recruiting; Vancouver; Hybrid; CAD 120,000 - 150,000; Full time

Job summary

Swim Recruiting is seeking an Edge Engineer to own the on-device computer vision pipeline powering its in-store analytics platform. You will deploy and optimize workloads on constrained hardware in live retail environments, tackling real-world challenges as the system scales across sites.

Expect hybrid work: 2 days on-site in Vancouver, with a startup culture that values ownership, rigor, and fast, but thoughtful delivery.

Qualifications

• 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.

Responsibilities

• 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.
• Own a real system that is already in customers' stores, with clear and unsolved technical problems in front of you.
• Shape their technical direction as part of a high-impact team during a growth phase.

Skills

Edge deployment

Python programming

Multi-object tracking

Evaluation & benchmarking

Startup mindset

Production inference

Tools

TensorRT

ONNX Runtime

OpenVINO

TFLite

NVIDIA Jetson

Job description

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.