Software Engineer, Verifications Platform

How you’ll make an impact

• Design, build, and maintain scalable backend services that power automated verification workflows, financial data integrations, and approval decisioning.
• Develop distributed systems, APIs, and event-driven services that improve the scalability, reliability, and reuse of verification capabilities across multiple lending products.
• Build platform capabilities that enable reusable financial data connections, streamline connection lifecycle management, and reduce operational overhead for internal engineering teams.
• Partner with Machine Learning, Product, Risk, Fraud, and Compliance teams to integrate data, decisioning logic, and risk models into production systems while maintaining correctness and auditability.
• Improve system reliability through comprehensive testing, monitoring, observability, and operational best practices for business-critical services.
• Contribute to the evolution of the verification platform by improving architecture, engineering standards, and shared infrastructure that accelerates product development across Upstart.

Minimum Qualifications

• 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.

Preferred Qualifications

• 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.