• Engineered a system to dark launch new player skill estimation methods and implemented ML model versioning in a Java microservice for 150M+ monthly League of Legends players.

• Implemented a new win prediction algorithm and ML model in Java, improving accuracy by 6%.

• Built data pipelines to analyze player engagement with progression systems using PySpark and Tableau.

• Rewrote the Splunk Enterprise reports page in Splunk's React component library, serving 6M+ monthly views.

• Developed a library to dynamically generate components in TypeScript based on a HTML string input.

• Worked on DRIVE Constellation platform, managing autonomous vehicle simulation infrastructure using Python.

• Created microservice to interface platform with our largest client's hardware

• Implemented architecture to allow for efficient autonomous vehicle simulation across computing clusters.

• Continued development on internal tools to allow for quick iteration and to ensure compatibility with hardware using Docker.

• Integrated AWS Rekognition into the product auditing pipeline which decreased product tagging costs by 50%.

• Created Prometheus metrics to track the performance of various API endpoints and cron jobs.

• Updated Backbone.js and React frontend alongside Tornado backend for various internal tagging tools.

• Wrote cron jobs in Python to prioritize product tagging impacting over $20 million of annual revenue