David Ding Email & Phone Number

New York, NY, US

Building out CockroachDB's cloud offerings, with a focus on unifying how our multiple cloud products are orchestrated and operated on Kubernetes.

New York, New York, US

I built the initial backend from scratch, putting a complete build/deployment process into place on AWS along with scaffolding around the API, error debugging, analytics, notifications, and more. After doing this, I transitioned into an advisory role, where I continue to provide technical guidance and mentorship.

New York, New York, US

• Engineering lead of the platform stability team: designed cost-effective infrastructure that could handle unpredictable extreme spikes in traffic.
• Maintained 99.95% uptime during a period of extremely rapid growth. My work included adding rate limiting throughout our systems to prevent overloads, separating out vital systems into isolated microservices.
• Significantly extended company runway by reducing infrastructure costs by 50%. Doing this involved programmatically detecting and rewriting bad DB queries, cutting away half of our DB replicas, adjusting inefficient.
• Built the backend for and maintained the nation’s most widely used contact tracing app, SafePass, with over 1M users. Navigated us through rapidly shifting societal conditions, and official collaborations with various.

Mountain View, CA, US

• My team took highly noisy Google Maps business updates from numerous sources, like ad feeds and user edits, and auto-matched them with existing businesses, ensuring that a user would see all business info in a single.
• Achieved a 30% relative gain in the pipeline’s precision to over 90% (while maintaining recall), by improving, retraining, and maintaining the ML model, and engineering thread-safe model features with 2-second p99.
• Developed online indexing methods to retrieve matching candidates for an input business, ensuring performance while processing ∼1000 index updates per second.

Mountain View, CA, US

Published a paper (arXiv:1709.06680) in interpretable ML models: specifically, extending existing highly interpretable lattice-based models to deeper and more flexible neural networks.

Pasadena, CA, US

Algorithms, Intro to Computer Programming, Fundamentals of Computer Programming

Mountain View, CA, US

Designed, coded, tested, and executed a tool to automate the transfer of live monitoringservices from one cloud service to another, and verify the monitoring services' integrity, inpreparation for the eventual infrastructure revision of a major monitoring system.

Master Of Science (M.S.), Computer Science (Machine Learning)

B.S, Mathematics, Computer Science