Were seeking an experienced Computer Vision Engineer to develop an edge AI system for sports field usage monitoring and predictive maintenance. Youll 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.
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-expected :
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
offered :
Opportunity to work on innovative, meaningful projects across multiple industries
Access to modern technologies and best development practices
Collaborative, idea-friendly environment where your voice matters
Career growth through challenging work, mentorship, and internal knowledge sharing
Flexible working arrangements
Supportive culture focused on progress, innovation, and work-life balance
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of foreign language classes
sharing the costs of professional training & courses
