Software Engineer, Verifications Platform

At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.

As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for...

We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that s...

Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience) and 3+ years of professional software engineering experience.
3+ years of experience developing backend applications using Kotlin, Java, or another object-oriented programming language.
Experience designing, building, and operating distributed systems, including service-to-service APIs and event-driven architectures.
Experience building and maintaining scalable backend services that process business-critical or financial data in production environments.
Experience contributing to decision engines that integrate with machine learning models to evaluate signals..
Experience writing production-quality code supported by automated testing, monitoring, and observability practices.
Knowledge of financial services, lending, fraud prevention, identity verification, or other risk-sensitive systems.
Experience integrating third-party financial data providers or APIs such as Plaid or similar platforms.
Experience building rule engines, workflow orchestration platforms, or automated decisioning systems.
Knowledge of how machine learning models are integrated, monitored, and evaluated within production systems.
Experience building reusable platform services or shared infrastructure supporting multiple engineering teams..