Principal AI Architect
About the role
SecurityScorecard is the global leader in cybersecurity ratings, with over 12 million companies continuously rated, operating in 64 countries. Founded in 2013 by security and risk experts Dr. Alex Yampolskiy and Sam Kassoumeh and funded by world-class investors, SecurityScorecard’s patented rating technology is used by over 25,000 organizations for self-monitoring, third-party risk management, board reporting, and cyber insurance underwriting; making all organizations more resilient by allowing them to easily find and fix cybersecurity risks across their digital footprint. Headquartered in New York City, our culture has been recognized by Inc Magazine as a "Best Workplace,” by Crain’s NY as a "Best Places to Work in NYC," and as one of the 10 hottest SaaS startups in New York for two years in a row. Most recently, SecurityScorecard was named to Fast Company’s annual list of the World’s Most Innovative Companies for 2023 and to the Achievers 50 Most Engaged Workplaces in 2023 award recognizing “forward-thinking employers for their unwavering commitment to employee engagement.” SecurityScorecard is proud to be funded by world-class investors including Silver Lake Waterman, Moody’s, Sequoia Capital, GV and Riverwood Capital.
SecurityScorecard is hiring a Principal Software Architect to lead the system design of our agentic AI capabilities and serve as a technical anchor for engineering teams building the next generation of our platform. This is an individual contributor role reporting to the Chief Architect, alongside a Front-end Architect and a Data-focused Architect, and partnering closely with engineering leadership, Product, and Data Science. We're looking for someone who can go deep technically and also move a room: an architect whose designs get adopted because they're well-reasoned and well-communicated, not just handed down. You will lead the system design for how agentic features are built, integrated, and operated reliably at production scale, working in close partnership with our Data Science team, who bring the model and ML depth. Your focus is the system: orchestration, state and memory, tool boundaries, failure semantics, and how all of it composes with the wider platform.