Softengi
AI

Computer Vision Engineer

Softengi · UA

Actively hiring 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

  • 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)
  • 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)
  • 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
  • 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
  • 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)
  • 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

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

Used for matching and alerts on DevFound
Machine Learning Computer Vision Ai
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