Software Engineer, Verifications Platform

Upstart Upstart · Fintech · Remote · Engineering

Upstart is a fintech company that uses AI to improve access to credit. This Software Engineer role on the Verifications Platform team will build and scale backend services for automated approval decisions, verification workflows, and fraud detection. The role involves integrating with third-party data providers, ML models, and internal rule engines to ensure accurate, compliant, and automated lending decisions. The goal is to achieve fully automated decisioning while maintaining risk correctness and compliance.

What you'd actually do

  1. Design and build backend services that power verification orchestration, risk evaluation, and automated approval decisions.
  2. Develop and evolve rule engines and decisioning systems to increase automation coverage across products.
  3. Integrate external data providers (e.g., Plaid) into resilient, scalable workflows.
  4. Improve document automation pipelines including classification, extraction, and fraud detection systems.
  5. Build and maintain APIs, Kafka events, and service contracts that enable product teams to consume verification capabilities.

Skills

Required

  • Bachelor's degree in Computer Science
  • 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

Nice to have

  • Experience building rule engines, decisioning systems, or risk evaluation platforms
  • Experience working with financial services, lending, fraud detection, or identity verification systems
  • Experience integrating third-party APIs and external data providers
  • Familiarity with workflow orchestration systems (e.g., Temporal)
  • Experience working with Kafka or event-driven systems
  • Exposure to ML model integration in production systems
  • Experience working with financial data platforms, connection lifecycle management, or reusable data access systems
  • Familiarity with systems that support multi-account, multi-product, or consent-aware financial data workflows

What the JD emphasized

  • fully automated decisioning
  • risk correctness
  • compliance integrity
  • automated approval decisions
  • automated workflows