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AI Referee Decision Review Assistant Engineer

Claire Schneider

Contract · Job · Madrid

About the role

The review tool you'll build gets used live during broadcast, not after the fact. Fast-moving team, tight broadcast windows, no room for a model that takes ten seconds to return an answer when a referee is waiting on a call. Your role is to build a computer vision assistant that flags likely infractions — offside lines, boundary calls, contact plays — from multiple camera angles in near real time, giving review officials a fast first read before they make the final call. We ship during live events, so there's no long QA cycle to hide behind. What you build gets tested under real broadcast pressure within weeks of starting.

Responsibilities

  • Build low-latency computer vision models for live infraction detection
  • Fuse multiple camera angles into a single confidence-scored recommendation
  • Optimize inference time to fit within broadcast review windows
  • Work directly with review officials to calibrate what counts as a flag
  • Debug model misses immediately after live events with the ops team

Requirements

  • Strong computer vision background with real-time inference experience
  • Comfortable with PyTorch or TensorFlow model optimization
  • Experience with Docker-based deployment under latency constraints
  • Able to work occasional live event hours during broadcasts

Benefits

  • Remote contract with event-based bonuses
  • Access to press-level seating for select live events
  • Fast-paced environment with visible, immediate impact
  • Renewable engagement tied to broadcast season

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Categories

AI & Machine Learning Engineers

Skills

python computer vision pytorch tensorflow docker

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Job Type

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Level

Mid-Level

Language

English

Salary Range

Negotiable / Competitive

AI Expertise

AI & Machine Learning Engineers

Skills

python computer vision pytorch tensorflow docker

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