Software Engineer, Upstart

• 4+ years of professional software engineering experience
• Experience designing and building scalable backend systems in languages such as Java, Kotlin, Go, or Python
• Experience developing and operating distributed systems, including service-to-service APIs and event-driven architectures
• Experience contributing to a decision engine that integrates with machine learning models to evaluate signals.
• Experience writing production-quality code with testing and monitoring
• Bachelor’s degree in Computer Science or equivalent practical experience

Desirable

• Upstart’s Verifications Decisioning Engineering team builds the backend platform that powers automated verification, fraud detection, and approval decisions across all lending products
• The team owns verification logic, risk signal generation, document automation, bank account integrations, and rule-based decisioning systems that enable near-instant funding experiences. Our north-star goal is to achieve fully automated decisioning while maintaining risk correctness and compliance integrity
• As a Software Engineer II you will build and scale backend services that power automated approval decisions, verification workflows, and fraud detection systems
• You will work on distributed systems that integrate with third-party data providers, machine learning models, and internal rule engines to power accurate, compliant, and highly automated lending decisions, while collaborating cross-functionally to optimize the verifications journey, and contributing to the development of our next-generation decisioning and automation platform
• Design and build backend services that power verification orchestration, risk evaluation, and automated approval decisions
• Develop and evolve rule engines and decisioning systems to increase automation coverage across products
• Integrate external data providers (e.g., Plaid) into resilient, scalable workflows
• Improve document automation pipelines including classification, extraction, and fraud detection systems
• Build and maintain APIs, Kafka events, and service contracts that enable product teams to consume verification capabilities
• Partner with ML engineers to productionize risk models and ensure decision correctness at scale
• Contribute to platformization efforts to standardize Verifications stages and enable cross-product reuse
• Strengthen compliance, data integrity, and observability across verification systems