About the Role We are building a next-generation AI-driven anti-cheating system for competitive strategy games.
Unlike traditional fraud detection, our challenge sits at the intersection of:
- 🎮 Game AI & player behavior modeling
- 🧠 Reinforcement learning & decision systems
- 🔍 Anomaly detection under adversarial conditions
You will work on identifying non-obvious, strategic cheating behaviors in complex environments where players actively adapt to detection systems. This is not rule-based detection — this is behavioral intelligence at scale.
What You’ll Do 1️⃣ Behavioral Modeling & Detection
- Design machine learning / deep learning models to detect cheating patterns
- Model player behavior sequences, strategies, and anomalies
- Build systems that distinguish:
- high-skill play vs. AI-assisted play
- natural variance vs. exploitation
2️⃣ Anti-Cheating System Design
- Develop scalable detection pipelines (offline + real-time)
- Build feature systems from gameplay logs / event streams
- Design evaluation frameworks for detection accuracy & robustness
3️⃣ ML / DL / Advanced Techniques
- Apply and experiment with:
- sequence modeling (RNN / Transformer-based)
- anomaly detection
- graph-based or behavioral embeddings
- Explore intersections with:
- reinforcement learning
- game-theoretic modeling
- adversarial ML
4️⃣ Collaboration with AI & Engineering Teams
- Work closely with:
- Gameplay AI / RL researchers
- Backend / data engineering teams
- Translate models into production systems
What We’re Looking For
✅ Core Requirements
- 4+ years in Data Science / Machine Learning roles
- Strong foundation in:
- deep learning
- statistical modeling
- Experience in one or more of:
- fraud detection / AML
- risk modeling
- anomaly detection
- behavioral analytics
✅ Strong Signals (Big Plus)
- Experience with:
- sequence models (LSTM / Transformer)
- large-scale behavioral data
- real-time detection systems
- Exposure to:
- reinforcement learning
- game AI
- adversarial systems
✅ Technical Stack
- Python (must)
- PyTorch / TensorFlow
- SQL / data pipelines
- Experience working with large-scale datasets
Why This Role is Interesting
- 🚀 Work on problems similar to fraud detection at scale — but harder
- 🎯 Direct impact on real-money / competitive environments
- 🧠 Blend of:
- ML research
- production systems
- game AI
- 🌍 Fully remote, globally distributed team
Location & Visa
- 🌏 Remote-first (global team)
- 🇯🇵 Japan relocation supported (visa sponsorship available for qualified candidates)
Who This Role is Perfect For
- Data scientists bored with “dashboard ML”
- Fraud / AML experts who want more complex, adversarial systems
- ML engineers who want to work closer to decision intelligence & behavior modeling