About The Role
We're seeking an experienced Computer Vision Engineer to develop an edge AI system for sports field usage monitoring and predictive maintenance. You'll build and deploy YOLOv8 models on NVIDIA Jetson devices to detect and track players on synthetic turf fields in real-time, generating actionable insights for maintenance crews and coaches.
Key Responsibilities:
Train, optimize, and deploy custom YOLOv8 object detection models for player tracking
Configure and manage Frigate on NVIDIA Jetson edge devices for sub-100ms inference
Develop zone-based tracking algorithms and wear pattern analysis
Build MQTT pipelines for streaming detection events to cloud infrastructure
Fine-tune models based on real-world field testing and edge deployment challenges
Requirements:
Strong experience in computer vision, including image processing, feature extraction, object detection, tracking, and classification techniques
Proven experience with YOLO object detection models (v5, v8, or similar)
Hands-on deployment of models on edge devices (preferably Jetson, embedded systems)
Strong Python proficiency for model development and optimization
Experience with real-time video processing and performance optimization
Problem-solving mindset for production deployment challenges
Nice-to-Have:
Frigate NVR or similar video analytics platform experience
NVIDIA Jetson device deployment
MQTT or IoT messaging protocols
Model quantization and edge inference optimization
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