ML scientist bidding optimization

adjoe builds technologies for mobile app growth and monetization. Its Playtime Arcade and adjoe Ads products cover rewarded engagement and programmatic in-app advertising, connecting advertisers, publishers, and users.

• Translate state-of-the-art research into robust, production-ready code optimized for sub-millisecond latency
• Architect auction mechanics, real-time bidding engines, and allocation algorithms
• Apply reinforcement learning, objective-based optimization, and control theory to bidding, pricing strategy, and budget pacing
• Handle large-scale Bayesian inference and sparse, high-dimensional advertising data
• Architect scalable data pipelines that feed real-time optimization models
• Lead architectural debates and map business goals to mathematical objective functions with Product Managers and Engineers
• Mentor junior scientists and drive the team’s research direction

• PhD in Computer Science, Statistics, Mathematics, or Physics, or an MSc with extensive industry research
• Peer-reviewed publications in Computational Advertising, Auction Theory, Mechanism Design, or Game Theory
• 4+ Years optimizing complex systems through automated algorithms in AdTech, marketplaces, or high-frequency trading
• Deep theoretical and practical expertise in objective-based optimization, control theory, or reinforcement learning applied to bidding, pricing strategy, and budget pacing
• Strong proficiency in Python and related stacks
• ** Nice to have:
  ** None

Relocation support to Hamburg, Germany is available