Senior Software Engineer, Upstart

• 4+ years of experience building backend systems in production, ideally powering consumer-facing products
• Strong technical depth in distributed, event-driven architectures (Kafka, SQS, Kinesis, MQ) including idempotency, retries, DLQs, and ordering
• Proficiency in an OO language (Java, Kotlin, C#, Python, Ruby) and comfort with relational databases, caching layers, and API design
• Demonstrated ability to ship zero-to-one systems or products, making tradeoffs in ambiguous environments
• A track record of connecting backend work to product outcomes (conversion, reliability, payments, onboarding)
• Experience designing with observability and reliability in mind (metrics, traces, logs, SLOs)
• Active use of AI tools in engineering workflows beyond autocomplete, with curiosity about how AI changes how teams build
• Strong written and verbal communication skills; able to explain complex systems and tradeoffs clearly

Desirable

• Recurring Credit is one of Upstart’s most ambitious new product bets: a mobile-first line of credit designed to replace payday loans, cash advance apps, and subprime credit cards with something safer and more sustainable
• The team is small, senior, and moving quickly—structured more like a startup inside Upstart than a traditional squad. We’re building both a new product and a new model for how engineering teams can operate in an AI-native way: lean, fast, and deeply focused on outcomes for underserved consumers
• Design and build core backend systems that power instant disbursements, subscription billing, and repayment schedules
• Develop distributed, event-driven services that guarantee reliability and scale—covering retries, ordering, schema evolution, and observability
• Translate product intent into customer impact by collaborating closely with product and design to ship high-quality user-facing features quickly
• Leverage AI-native workflows (e.g., Copilot, Cursor, LLM-powered tooling) to accelerate coding, testing, and analysis
• Ensure trust and transparency at the system level by instrumenting metrics, logs, and traces tied to business and user outcomes
• Balance speed and quality in a greenfield codebase, helping set the technical direction for a product with zero-to-one momentum and long-term scale
• Shape engineering culture and velocity through thoughtful APIs, automation, and scalable practices