At Abnormal Security, we use artificial intelligence to catch cyberattacks like ransomware, vendor fraud, and BEC. As the Head of Message Security Detection, I lead the Org responsible for stopping these advanced email attacks and providing AI solutions that protect more than 15% of the Fortune 500. I'm proud to support a cross-functional division of 50+ team members composed of Machine Learning Engineers, Software Engineers, Data Scientists, and Analysts. We're continuing to solve a hard machine learning challenge, which requires crafting solutions against adversarial, evolving attacks at increasingly large scale -- come join us!

The Detection Serving & Signals team provides an ML Infra Platform that enables engineers to quickly and effectively improve Abnormal’s threat attack detection across all of our products, and to introduce new product lines. We're hiring! If you're interested in joining a fast-growing startup and building out systems to bring us closer to being the top cloud office cybersecurity company, feel free to message me or check out out some of our open roles at https://careers.abnormalsecurity.com/open-roles

Architecting real-time, near real-time, and batch indexing applications & frameworks to power Pinterest's recommendation surfaces

• Pixie: bipartite board-and-pin graph recommendation service that serves as one of the largest candidate sources for HomeFeed and Related Pins. Rearchitected the system to provide functionality to easily experiment with lightweight scoring and incorporate new signals into the graph index. • User signal development platforms that provide signals for query understanding, ranking, and retrieval across multiple Pinterest surfaces (HomeFeed, Related Pins, Search, Ads)

Homefeed/Recommendations: Infrastructure and Product Enablement • Pinnability ranking infra: DSL / scoring library, performance optimizations • Integrating different sources of content into the Homefeed and ensuring that the Homefeed has both quality and quantity of content