Job Description

We develop predictive and generative models of player behavior and the game systems that act on them, while also researching and optimizing the core mathematical models behind our games— particularly slot machines. In this role you will translate ambiguous product questions into well-posed modeling problems and deliver solutions that are engaging, profitable, compliant with global regulatory standards, and reliable under production traffic and constraints. You will collaborate with game designers, product managers, and engineers in a fast-paced, on-site, consensus-driven environment, driving both research and product impact. We're looking for a researcher in Mathematics, Statistics, Computer Science, or a related quantitative field, with strength in probability, statistical/ML modeling, and game mathematics. Fluency in Chinese is required, and you must be able to work on-site in Irvine, CA. Responsibilities Player Behavior & Game-System Modeling Develop models of player behavior—engagement, monetization, retention, anomaly detection— and the systems that act on them. Translate loosely-specified product questions into modeling problems and select appropriate methods across probabilistic and generative models, gradient boosting, reinforcement learning and bandits, and constrained optimization. Apply player lifetime value estimation and retention analysis to inform game features and design decisions. Game Mathematics Design & Optimization Develop and refine core math models for slot machines and other games, meeting target RTP, hit frequency, and volatility goals. Design and verify random mechanics, bonus rounds, free spins, and jackpots through combinatorial analysis and detailed probability calculations. Balance player engagement, regulatory requirements, and long-term profitability through innovative mathematical solutions. Data, Simulation & Evaluation Work with large, imbalanced, real-world datasets; diagnose data quality, distribution shift, and leakage, and design evaluation that reflects true model performance. Perform large-scale simulations and data analyses (Python, MATLAB, R, or custom code) to forecast game performance, validate math models, and optimize retention. Production & ML Workflow Take models to production with clean, reproducible code; collaborate on deployment, monitoring, and inference constraints. Build and operate ML pipelines on cloud infrastructure (AWS), including training, deployment, and monitoring of production models. Regulatory Compliance & Certification Support Prepare math models, simulation reports, and documentation for certification by global regulatory bodies (e.g., GLI, BMM) across North America, Europe, and Asia. Cross-Functional Collaboration & Continuous Improvement Translate complex math/ML concepts into compelling gameplay and clear player communication for both technical and non-technical audiences. Play-test in-development games, analyze live performance data, and maintain design documentation (par sheets, pay tables, control files). Required Qualifications PhD in Mathematics, Computer Science, Statistics, or a related quantitative field (new graduates welcome), OR an MS with 2–3 years of industry experience as an Applied Scientist or ML Engineer. Strong expertise in combinatorics, probability theory, and statistics, with demonstrable skill in mathematical modeling for game design. Strong data intuition: ability to identify distribution shift and leakage and to select metrics appropriate to the problem. Solid software engineering fundamentals—version control, testing, reproducibility—and sound judgment on when a prototype must become production software. Demonstrated ability to drive a modeling problem from formulation to a working solution independently. Experience with simulation/programming tools such as Python. Fluency in Chinese (Required) and the ability to communicate complex concepts to technical and non-technical audiences. Must be able to work on-site in Irvine, CA 92618. Preferred / Nice to Have Familiarity with AWS and the end-to-end ML workflow, from modeling through deployment, monitoring, and inference constraints. Depth in reinforcement learning and bandits, probabilistic/generative modeling, or constrained/convex optimization and linear programming. Experience with agentic AI systems or generative models (e.g., LLMs, diffusion models). Background in gaming, simulation, recommender systems, or fraud/anomaly detection. Research experience with publications; experience with both land-based and online games. Familiarity with programming tools C++. Knowledge of player behavior modeling and player lifetime value estimation. Passion for gaming and a creative yet analytical approach to game design. #J-18808-Ljbffr

Job Tags

Similar Jobs