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AI Matchmaking Fairness Engineer

Pelvex Tech

Hybrid · $100k – $130k / year · Full-time · Posted 3 months ago

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About the role

Have you ever quit a match five minutes in because the other team was clearly stacked before it even started? Our churn prediction and dynamic difficulty tools already respond to how a single player is doing, but matchmaking itself still groups players mostly by raw skill rating, which produces lopsided teams more often than players notice. This role builds matchmaking models that balance team fairness alongside skill and queue time, so matches feel competitive from the opening minute.

Responsibilities

  • Build matchmaking models balancing team fairness, skill, and queue time
  • Run live experiments measuring match competitiveness and player retention
  • Work with the player churn team to validate fairness against quit rates
  • Tune matchmaking tradeoffs across different game modes

Requirements

  • Experience with matchmaking, ranking, or reinforcement learning systems
  • Strong SQL and statistics skills for large-scale player data
  • Comfortable working two days a week from the studio
  • Interest in competitive game balance and player experience

Benefits

  • Hybrid schedule with two studio days per week
  • Competitive salary and equity
  • Health, dental, and vision insurance
  • Game studio perks and early access to new titles

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Categories

AI & Machine Learning Engineers Data Science & Analytics

Skills

python sql statistics reinforcement learning

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

AI & Machine Learning Engineers Data Science & Analytics

Skills

python sql statistics reinforcement learning

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