Remote Digital Signal Processing Engineer Jobs in Oregon

The Team: 

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, Verifications Platform, 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.

How you'll make an impact

• 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.
• Build platform capabilities that enable reusable financial data connections across products while improving automation, observability, and connection lifecycle management.
• Partner with underwriting, and verification teams to streamline Plaid data usage and accelerate the adoption of financial signals in decisioning models and automated workflows.
• Collaborate with ML teams 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.

Minimum Qualifications 

• Bachelor's degree in Computer Science and 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.

Preferred Qualifications

• 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.

Position location This role is available in the following locations: Remote

Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are expected to still spend high quality time in-person collaborating via regular onsites. The in-person sessions' cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

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