Computer Vision Engineer to $7000

Role Overview

Designs and implements the visual feature extraction pipeline, ensuring high-quality input data for the ML model from multi-camera capture system

Key Responsibilities

Phase 0: Pilot 

• Design and deploy 3-camera capture system (top-down + 2 oblique)
• Implement cross-polarized lighting setup for glare elimination
• Develop visual feature extraction algorithms:
  • Skin blanching detection
  • Contact patch area measurement
  • Finger flexion analysis (keypoint tracking)
  • Micro-tremor detection (10-20 Hz)
• Synchronize camera streams with hardware frame-lock
• Collect and curate training dataset (100+ matches)

Phase 1: Demo 

• Optimize feature extraction for real-time performance (<8ms budget)
• Implement confidence scoring for feature quality
• Handle challenging conditions (varied lighting, athlete positioning)
• Support broadcast integration with visual debugging tools
• Refine calibration procedures based on demo feedback

Phase 2: Production 

• Implement failover and redundancy for camera failures
• Optimize for 98%+ uptime during live events
• Develop automated quality monitoring and alerting
• Support LED synchronization (Art-Net/DMX integration)

• Production-grade error handling and recovery

   

Required Technical Skills

Must Have:

• 5+ years experience in computer vision engineering
• Expert-level knowledge of OpenCV and image processing techniques
• Experience with high-speed camera systems (120+ FPS)
• Strong understanding of optical phenomena (lighting, polarization, color science)
• Experience with multi-camera synchronization and calibration
• Proficiency in C++ and Python for real-time CV pipelines

• Experience with GPU-accelerated image processing (CUDA, cuDNN)

   

Strongly Preferred:

• Experience with industrial vision systems or broadcast/entertainment applications
• Knowledge of color-based feature extraction (blanching, perfusion analysis)
• Experience with pose estimation and hand/finger tracking (MediaPipe, OpenPose)
• Background in optics and lighting design for machine vision
• Experience with GigE Vision or USB3 Vision camera protocols

• Familiarity with embedded vision systems or edge deployment

   

Nice to Have:

• 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

   

Required Soft Skills

• 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)

   

Success Metrics

• Camera system operational with <0.1% frame drop rate
• Feature extraction latency <8ms (P95)
• Visual features correlate with grip force (0.4-0.7 per signal)
• System handles diverse athlete characteristics (6 Fitzpatrick skin tones)
• 100+ matches captured with synchronized reference data