Senior Applied Computer Vision Engineer
Jobgether
Senior Applied Computer Vision Engineer
Senior Applied Computer Vision Engineer position with a partner company, focused on transforming sports video into actionable insights through computer vision and machine learning. Develop, optimize, and scale production-grade vision systems for object detection, tracking, camera calibration, and video analytics. Remote-first role based in Netherlands, requiring extensive experience in production CV systems, Python, PyTorch, and geometric computer vision.
Senior Applied Computer Vision Engineer
Senior Applied Computer Vision Engineer position with a partner company, focused on transforming sports video into actionable insights through computer vision and machine learning. Develop, optimize, and scale production-grade vision systems for object detection, tracking, camera calibration, and video analytics. Remote-first role based in Netherlands, requiring extensive experience in production CV systems, Python, PyTorch, and geometric computer vision.
Salary
Core Qualifications
Technical (Must-have)
Soft Skills
Preferred Qualifications
Technical (Nice-to-have)
Key Responsibilities
- Develop, enhance, and maintain production-ready computer vision models for sports video analytics, including player and ball detection, tracking, event recognition, and identity association.
- Design and improve geometric computer vision solutions such as camera calibration, homography estimation, field registration, and coordinate mapping.
- Evaluate existing computer vision pipelines, identify performance bottlenecks, analyze failure modes, and implement practical improvements.
- Adapt machine learning models and vision pipelines to support new sports, leagues, stadiums, camera configurations, and varying video quality conditions.
- Design and execute experiments covering dataset creation, augmentation, model training, fine-tuning, evaluation, and production readiness.
- Collaborate with data, software, platform, and DevOps teams to deploy scalable ML solutions into production.
- Define evaluation metrics, testing strategies, and quality assurance processes to ensure consistent model performance.
- Lead technical initiatives from early research through deployment, communicating decisions and trade-offs.