We are seeking a talented Computer Vision Engineer with expertise in human body recognition and tracking to join our team. The primary objective is to design, develop, and implement cutting-edge computer vision algorithms for tracking human activity and performance in sports, focusing on running, jumping, and other dynamic movements. This role will directly contribute to enhancing performance analysis, athlete monitoring, and injury prevention through real-time tracking of attributes such as acceleration, speed, and movement patterns.
Technical PoC aims to demonstrate and validate the feasibility of the application’s technical implementation in the most cost-effective manner. It aims to provide a practical evaluation of the solution’s potential, while minimizing risks and ensuring efficient use of resources for informed decision-making.
- 3+ years of experience in computer vision, with a focus on human body recognition, pose estimation, or activity tracking.
- Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and computer vision libraries (e.g., OpenCV, Mediapipe).
- Proficient in Python and C++, with experience in deploying machine learning models.
- Familiarity with key algorithms for human pose estimation (e.g., OpenPose, DeepLabCut) and tracking (e.g., Kalman filters, optical flow, or CNN-based trackers).
- Strong understanding of linear algebra, optimization techniques, and statistics.
- Previous work in human activity recognition, preferably in sports, fitness, or health industries.
- Experience in optimizing computer vision models for real-time, low-latency performance on mobile or edge devices.
Preferred Qualifications:
- Experience in integrating computer vision models with IoT devices or wearable sensors.
- Knowledge of biomechanics and sports science for better alignment with performance metrics.
- Familiarity with 3D human pose estimation techniques and tools.
- Strong understanding of real-time processing pipelines and cloud computing platforms (AWS, GCP, Azure).
