Senior Machine Learning Engineer - Anti-Cheat AI - Security

April 6

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A5 Labs

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A5 Labs is a pioneering company that focuses on data, machine learning, and blockchain technologies to enhance the integrity and fairness of competitive online games. Comprising a team of tech entrepreneurs, AI scientists, and passionate gamers, A5 Labs aims to address the challenges faced by the online gaming industry, including issues related to game integrity and player matchmaking. The company leverages advanced analytics, artificial intelligence, and blockchain platforms to create transparent gaming experiences and ensure fair play for all users.

Game Theory Research • Machine Learning • Business Intelligence • Online Gaming • Game Design

51 - 200 employees

🎮 Gaming

🤖 Artificial Intelligence

📋 Description

• 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. • 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. • 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. • 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. • 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.

🎯 Requirements

• 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 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. • Experience in reinforcement learning, inverse reinforcement learning, or multi-agent systems.