Senior Machine Learning Engineer, Anti-Cheat Ai- Security (Remote)

Key Responsibilities

Machine Learning Pipeline Development & Deployment

• Design and deploy scalable ML/DL pipelines for real-time fraud and bot detection in high-volume competitive gaming networks.

• Optimize deep learning models for low-latency inference and real-time decision-making.

• Automate model training, tuning, and deployment using MLOps best practices.

• Implement distributed computing techniques to process large-scale poker data efficiently.

Automation & Bot Detection

• Develop and deploy real-time bot detection models, leveraging behavioral biometrics, timing patterns, and clickstream analysis.

• Implement graph-based analytics to uncover multi-accounting automation, bot rings, and coordinated fraud.

• Optimize AI-driven countermeasures to detect hybrid human-bot play and adversarial AI threats.

Game Theory & Exploitative Modeling

• Support the integration of game-theoretic AI models into real-time detection pipelines.

• Develop exploitative modeling features to detect unnatural betting patterns.

• Implement multi-agent simulations to test and validate anti-cheat AI strategies.

MLOps & Engineering Best Practices

• Design and implement robust CI/CD pipelines for ML models in anti-cheat applications.

• Ensure high availability and fault tolerance for fraud detection systems.

• Optimize inference models for low-latency execution in production environments.

• Work with cloud platforms (AWS, GCP, or Azure) to deploy and scale AI security models.

• Monitor and log model performance, ensuring continuous improvement and retraining.

Cross-Functional Collaboration

• Work closely with data scientists, software engineers, and poker security experts to align ML solutions with business needs.

• Collaborate with game developers to integrate anti-cheat AI into poker platforms.

• Partner with poker analysts to fine-tune model accuracy and identify new threats.

Technical & Experience Requirements

Technical Skills

• Master’s or PhD in Computer Science, Machine Learning, AI, or a related field.

• 5+ years of experience in ML/DL model development, optimization, and deployment.

• Strong programming skills in Python, SQL, and distributed computing frameworks (Spark, Kafka, Kubernetes, etc.).

• Expertise in TensorFlow, PyTorch, or Scikit-learn for ML model development.

• Experience with real-time ML inference, feature engineering, and data pipelines.

• Hands-on experience with MLOps, cloud deployment (AWS, GCP, Azure), and Kubernetes/Docker.

• Familiarity with fraud detection techniques, adversarial AI, and anomaly detection.

• Experience working with large-scale structured and unstructured data for fraud modeling.

• Understanding of graph-based fraud detection and multi-agent AI techniques.

Preferred Experience

• Preferred experience working with real-time fraud detection systems in gaming, cybersecurity, or fintech.

• Knowledge of game theory, Nash equilibrium, and exploitative modeling.

• Familiarity with multi-accounting fraud detection and adversarial ML.