Machine Learning Engineer @ LoadComplete

Full-time; 2022/07 - Present

Machine learning engineer on the Applied ML team, focusing on building and integrating machine learning solutions into the studio’s game development and servicing processes.

• Led the design and development of the machine learning-based automated playtesting process for Frame Arms Girl: Dream Stadium and an upcoming title, which greatly contributed to bug discovery and design validation. This involved devising and implementing all of the system components behind this process and the model’s architecture.
• Designed and implemented the internal solution for building and deploying deep learning-trained game-playing agents in Unity. This solution addressed the limitations of the previous solution by featuring improved speed, scalability, and extensibility through features such as async action retrieval, dynamic batching, a plugin-based architecture, and more robust fault tolerance.
• Led the design and development of predictive modeling systems to support the servicing of our games and established stable, cloud-native MLOps for developing, servicing, and monitoring these models.
• Implemented advanced training algorithms and techniques for optimized and more effective model training.

GDC 2024

I presented the work on the automated playtesting process for Frame Arms Girl: Dream Stadium at GDC 2024 in a talk titled “Testing Empowered: Integrating ML-Based Playtesting in a Team with Limited QA Capacity”. Recording of the talk can be found here.

Skills

• Python
• PyTorch
• Polars
• FastAPI
• Docker
• Kubernetes
• Google Cloud Platform (GCP)
• MLOps / RLOps / DevOps
• Deep Learning
• Deep Reinforcement Learning
• Data Engineering
• Software Development
• Game Development
• Unity
• C#
• C++
• Git