We are seeking a talented Computer Vision Engineer to design and develop mirror-aware, multi-camera perception systems for real-time human pose tracking. The role involves building robust 2D/3D pose estimation pipelines, handling occlusions and reflections, and optimizing performance for wearable and mobile devices.
Key Responsibilities
Design and implement multi-camera and mirror-aware computer vision architectures.
Develop 2D and 3D human pose estimation systems.
Implement camera calibration, stereo vision, and multi-view reconstruction pipelines.
Perform mirror detection and reflection analysis using geometric consistency techniques.
Develop algorithms for 3D joint triangulation and reprojection validation.
Build real-time biomechanical analysis systems for posture, movement, and form correction.
Implement temporal filtering and synchronization between camera streams.
Optimize inference pipelines to achieve sub-100ms latency.
Support ONNX and TensorFlow Lite (TFLite) model deployment.
Create evaluation datasets, performance metrics, and automated testing frameworks.
Pose & Video Analytics
Human Pose Estimation
2D/3D Skeleton Tracking
Video Processing
Real-Time Streaming
Multi-Camera Systems
Temporal Filtering
Programming
Python
NumPy
SciPy
OpenCV
scikit-image
C++
Git
AI / Deep Learning
MediaPipe
YOLO
HRNet
ONNX
TensorFlow Lite (TFLite)
Edge AI
On-Device Inference
Preferred Qualifications
Experience with multi-camera or stereo camera systems.
Production deployment of Computer Vision applications.
Experience with Edge AI and mobile deployment.
Exposure to AR/VR, Robotics, Sports Analytics, or Fitness Technology.
Familiarity with CVPR, ICCV, or ECCV research literature.