AI Gateway team

• Designed and deployed 3 Java CRUD operations for 500+ reviewers to use to provide feedback on model inferences for retraining ML models used within the App Store Review pipeline, being used in production for incoming app submissions

• Made workflow with Docker and sidecars to update Cassandra tables with reviewer feedback; assisted in the development of App Store Review UI in React

• Implemented internal Kafka publisher-consumer notification system; wrote 10+ unit tests, added Splunk logging

• Presented to 70+ people, worked closely with 3+ teams to built product

• Created an Apple internal tool to maximize efficiency across a team of 15+ engineers, using Python, keyword extraction (RAKE and Cosine Similarity), internal APIs, Kubernetes, and Docker

• Deployed an algorithm to run on incoming problems, parse through and update a database of 60,000+ old problems to find the most similar ones, and attach them to each incoming problem

• Developed HVAC control algorithm based on indoor and outdoor PM2.5 measurements from smart thermostat sensors

• Performed data analysis and modeling to determine spikes of harmful quantities of PM2.5 in the air

• Collaborated with UC Berkeley researchers, professors, and students to gather data and produce hardware for PM2.5 analysis

• Requires mastery of advanced data science curriculum (Python, ML concepts, regression models, classification techniques, etc)

• Answered conceptual questions during office hours for 1000+ students in the class (DATA 100)

• Led and taught 20-person, small-group discussion sections on Random Forests

• 8 hours of commitment per week