Software Engineer, Verifications Platform

Upstart is a leading AI lending marketplace focused on reducing the cost and complexity of borrowing for Americans. The Software Engineer II on the Verifications Decisioning team will design and build backend services for verification workflows, integrating financial data and machine learning models to enhance automation and scalability.

Responsibilities:

• 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

Requirements:

• 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