Company Description
HomeTeam Network (HTN) delivers streaming technology for qualified, collegiate, Olympic, youth, and high school sports organizations. The company produces thousands of live and on-demand events every year across 42 different sports, serving audiences in Canada, the United States, and Europe. HTN focuses on reliable, high-quality sports broadcasting that scales from elite competitions to grassroots events. Team members collaborate with diverse sports partners and work with cutting-edge media technology. Joining HTN offers opportunities to influence how sports content is captured, processed, and experienced globally.

Employment type: Full-time, regular Employee

Compensation: 85k - 150k plus Equity participation

Role Description
You’ll build and run the computer vision behind those products: detection, tracking, automated camera framing and event recognition in live sports video. You’ll own how it behaves in production, not just how it scores offline. The footage is real-world: variable arena lighting, glare, netting and glass, small fast objects, imperfect camera placement and limited venue bandwidth. Models run in real time on GPUs in the cloud and at the edge, and people watch the output live.

What you’ll do

Design, train and ship detection and tracking models for players, ball or puck, and game events across several sports

Build automated camera framing that follows play at broadcast quality

Build video-based event detection for highlight generation, and evaluate it against our existing detection methods

Optimize real-time inference for GPU and edge hardware under latency, compute and bandwidth limits

Own the data loop: capture from our own game footage, annotation, dataset versioning,



and targeted collection when a failure mode shows up

Define evaluation that reflects what a viewer notices (a missed play, bad framing, the wrong moment in a highlight) alongside standard metrics

Monitor models in production across venues, sports and lighting conditions, and fix what drifts

Support camera calibration and venue geometry

Build tooling that lets operations staff diagnose and clear issues during a live event

Work with streaming, platform, product and customer-facing teams on what partners need

Required

Experience shipping computer vision models to production and keeping them running

Strong Python and hands-on PyTorch or TensorFlow

Solid grounding in object detection and multi-object tracking

Experience with event or action recognition in video, including finding when an event starts and ends

Real-time video pipeline experience with OpenCV, FFmpeg or GStreamer

Comfort with messy real-world footage: occlusion, motion blur, variable lighting, compression artifacts

Willingness to own a problem end to end on a small team, from data through deployment

Clear written English and experience working with distributed teammates

Nice to have

Sports video experience: broadcast automation, player or ball tracking, sports analytics

GPU and edge deployment: TensorRT, ONNX, NVIDIA Jetson, quantization

Camera calibration, homography, multi-camera geometry or PTZ control

Audio event detection or audio-visual fusion

Annotation tooling, active learning or semi-supervised learning

MLOps: experiment tracking, dataset and model versioning

Cloud deployment on AWS or GCP

Hands-on use of AI coding tools

Familiarity with hockey, baseball, football, soccer or basketball

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📌 Computer Vision Engineer (Manitoba)
🏢 HomeTeam Network
📍 Manitoba