Only individual in 82-person org to receive highest-possible performance rating of “Redefined Expectations” in 2023; again in 2024.

Developed NLU and generative models in TensorFlow to improve multilingual Siri parser accuracy for on-client domains, with a focus on scalability to low-resource languages. Designed, implemented, and deployed NLP software package for few-shot synthetic data generation of PII-free conversational language data, used by Siri teams for multilingual model training.

Implemented language models end-to-end with data pipelines, training pipelines, on-device testing. Used Docker, AWS S3, protobuf.

Collaborative project with Facebook AI. Researched and developed NLP models for hybrid extractive–abstractive text summarization and argument graph modeling.

Created conversation summarization benchmark on 40,000+ dialogues by implementing clustering and summarization pipeline with PyTorch, HuggingFace Transformers, sklearn. Used pandas, NumPy for data processing. Ran on Linux, used git.

Developed year-in-review statistics feature for 214,000 paid users, including database backfill and user-facing modals.

Coded frontend with React, Redux, JavaScript/JSX, jQuery; backend and database with Python, MongoDB, pymongo. Used Git, Jira, Unix, CircleCI.