Software Engineer on Scale Product Team

• Led project workstreams on: notifications (scaled back-end abstraction via queues), follow suggestions (ran countless A/B experiments with flows & triggers) and new user experience (reduced friction, increased retention)

• Built an email sending abstraction for sending one-off emails to support loads of 300m+ sends in short timeframes with batch processing while ensuring exact-once semantics, idempotency (Python, Redis, HBase, Kafka, Spark)

• Implemented browser push notifications to increase user engagement (Python, React, GraphQL)

• Reduced pages by 75% as team on-call lead via improving system reliability, driving cross-functional processes

Software Engineering Intern on Product Platform Team: Messenger Assistant

• Built new test infrastructure for e2e system health for voice assistant to improve alerting and debugging (Python)

• Wrote multithreaded load testing that parses test results, validates accuracy, and logs to databases (Python, ODS)

• Developed a low-latency alerting system to check test infra failures for on-call engineers (Scuba, Anomaly)

Software Engineering Intern on Distribution Product Team

• Developed and launched a new notification targeting search-intent users that increases engagement (Python)

• Created a scalable, reliable pipeline for the notification with checkpointing, monitoring (Python, Redis, HBase)

Semantic Scholar is a search engine that leverages AI expertise to help researchers find the most relevant information efficiently. This new service for scientific literature search and discovery focuses on semantics and textual understanding. It utilizes methods from data mining, natural-language processing, and computer vision to create powerful new search and discovery experiences.

• Developed and launched (under experimentation) a trending feed of popular research papers on the homepage of Semantic Scholar

• Improved social media scraper, increasing yield of social content referencing research papers by 510% (Python, AWS RDS & S3)

• Designed and created trending ranking algorithm and integrated it into data pipeline (Scala, Spark, Elasticsearch)

• Implemented the front-end for the trending feed on the homepage of Semantic Scholar (React, HTML/LESS)