Senior Machine Learning Engineer2019 - PRESENTApple
Technologies: Tools, Core ML, Git, PyTorch, TensorFlow
- Deployed high-performance deep-learning models to run on-device to improve inference speed and user privacy.
- Led the design of a deep-learning inference stack for on-device and cloud for first and third-party developers at Apple.
- Served as a tech lead and helped grow the team from three to ten engineers and researchers.
Senior Director of Engineering2015 - 2019Petuum
Technologies: Protocol Buffers, Kubernetes, Docker, GraphQL, C++, Spark, TensorFlow
- Worked as part of the founding team and helped to grow the engineering team from less than ten in 2016 to over 60 full-time engineers in the second quarter of 2018 (2Q18).
- Influenced—as a domain expert in machine learning, a trench worker and a manager—the product design and delivered quarterly internal platform product milestones from 3Q17-1Q18.
- Led the medical imaging team of four and personally developed best-in-class deep learning methods for chest X-rays to assist radiologists in diagnosis. The project led to the first contract for the company.
- Served as the tech lead and managed the back-end design of Petuum's AI platform that combined all stages of machine learning, including data cleaning, feature engineering, data and model visualization, deployment, and monitoring.
Software Engineer Intern2015 - 2015
Technologies: Tools, Facebook, Vowpal Wabbit, C++
- Contributed more than 10,000 lines of C++ code during my 3-month internship and helped initiate a large-scale distributed training system for Facebook newsfeed and ad algorithms.
- Developed a distributed large-scale logistic regression using LBFGS solver and parameter server-based synchronization.
- Benchmarked the implementation against Facebook's internal system and Vowpal Wabbit; showed that the implementation achieves high system throughputs and produces comparable to better models.
Software Engineering Intern2013 - 2013
Technologies: Tools, Google, Protocol Buffers, C++
- Optimized one of the world’s largest machine learning systems: Google’s ads algorithms for click-through-rate prediction.
- Developed a large-scale hyperparameter tuning framework to enable Google’s ads training system to discover the best hyperparameter settings for ML models based on convex and non-convex optimization methods. The system is used by dozens of teams and later published as Google Vizier.
Software Developer Intern2012 - 2012
Technologies: Spring, Python, SQL, Java
- Implemented a number of background tasks in the payment backend.
- Improved access control for internal payment handling.
- Developed a dynamic terminal folder navigator (to replace the "CD" in Linux) in a hackathon.