Jason Chen

Built backend infrastructure for large-scale AI training and inference workflows related to Gemini, improving reliability and performance of multi-tenant ML platforms and enabling high-throughput experimentation.

Designed backend systems and platform infrastructure for large-scale ML deployment, built model lifecycle and monitoring services, and optimized training and inference workflows to reduce iteration time.

Developed ML infrastructure and backend services for Siri knowledge and ranking systems, building scalable data pipelines and improving inference latency and data quality in production.

Built backend services and platform integrations for enterprise AI systems, implemented training and real-time scoring pipelines, and improved platform reliability, throughput, and observability.

Built backend services and APIs to enable platform integrations and cross-system commerce workflows through service-oriented architecture.

Designed ranking algorithms for marketplace matching, built backend integration tests, and contributed to a patent on dynamic pricing methodology.

Completed a Master of Science in Computer Science focused on advanced computing topics and systems engineering.

Completed a Master of Science in Statistics with emphasis on statistical modeling and analysis.

Completed a Bachelor of Science in Statistics covering probability, inference, and applied statistical methods.

Earned a Bachelor of Science in Statistics with foundational training in probability and statistical methods.

Completed a Bachelor of Applied Science in Physics with foundational training in physical sciences and quantitative analysis.

Completed a Bachelor of Science in Physics with comprehensive coursework in theoretical and experimental physics.