Computer Vision Engineer
Actively Hiring
Full-time Posted 4 months ago
Role overview
Designs and implements the visual feature extraction pipeline, ensuring high-quality input data for the ML model from multi-camera capture system
Responsibilities
- check_circle Design and deploy 3-camera capture system (top-down + 2 oblique)
- check_circle Implement cross-polarized lighting setup for glare elimination
- check_circle Develop visual feature extraction algorithms: Skin blanching detection Contact patch area measurement Finger flexion analysis (keypoint tracking) Micro-tremor detection (10-20 Hz)
- check_circle Skin blanching detection
- check_circle Contact patch area measurement
- check_circle Finger flexion analysis (keypoint tracking)
- check_circle Micro-tremor detection (10-20 Hz)
- check_circle Synchronize camera streams with hardware frame-lock
- check_circle Collect and curate training dataset (100+ matches)
- check_circle Optimize feature extraction for real-time performance (<8ms budget)
- check_circle Implement confidence scoring for feature quality
- check_circle Handle challenging conditions (varied lighting, athlete positioning)
- check_circle Support broadcast integration with visual debugging tools
- check_circle Refine calibration procedures based on demo feedback
- check_circle Implement failover and redundancy for camera failures
- check_circle Optimize for 98%+ uptime during live events
- check_circle Develop automated quality monitoring and alerting
- check_circle Support LED synchronization (Art-Net/DMX integration)
- check_circle Production-grade error handling and recovery
- check_circle 5+ years experience in computer vision engineering
- check_circle Expert-level knowledge of OpenCV and image processing techniques
- check_circle Experience with high-speed camera systems (120+ FPS)
- check_circle Strong understanding of optical phenomena (lighting, polarization, color science)
- check_circle Experience with multi-camera synchronization and calibration
- check_circle Proficiency in C++ and Python for real-time CV pipelines
- check_circle Experience with GPU-accelerated image processing (CUDA, cuDNN)
- check_circle Experience with industrial vision systems or broadcast/entertainment applications
- check_circle Knowledge of color-based feature extraction (blanching, perfusion analysis)
- check_circle Experience with pose estimation and hand/finger tracking (MediaPipe, OpenPose)
- check_circle Background in optics and lighting design for machine vision
- check_circle Experience with GigE Vision or USB3 Vision camera protocols
- check_circle Familiarity with embedded vision systems or edge deployment
Preferred qualifications
- Experience with NIR imaging or multi-spectral cameras
- Knowledge of photogrammetry and 3D reconstruction
- Experience with motion capture systems or sports analytics
- Background in signal processing for vibration/tremor detection
- Familiarity with broadcast equipment and professional video workflows
- Hands-on hardware expertise: Comfortable with physical camera setup and troubleshooting
- System thinking: Understand end-to-end pipeline from optics to ML model
- Attention to detail: Ensure data quality and consistency across diverse conditions
- Pragmatism: Balance theoretical perfection with practical constraints (time, budget)
- Field readiness: Willingness to travel for on-site deployments (2-3 trips to US)
Tags & Focus Areas
Machine Learning Computer Vision Ai