Staff Software Engineer (Credit Insights)

Company: Plaid

Location: New York, New York, United States {{REMOTE}}

Salary: $207.6k - $273.6k per year

Type: Full-time

Posted: 2026-09-02

About this role

  • The Credit Decisioning platform team is responsible for building best-in-class cashflow based insights products that enable lenders to make more holistic lending decisions and empower broader access to Credit products for prospective borrowers
  • We own the systems and tooling that form the platform to build and serve these insights at huge scale, partnering with our Data partners to release new products yearly
  • You will be defining the future architecture of Credit insights products and executing against an ambitious product roadmap
  • You will partner with our Product, Data Science, and Machine Learning team to iterate on and productionize new insights that enable our customers to make more holistic lending decisions
  • Leading technical architecture and execution across credit insights products: everything from data fetching and online feature serving for API requests, to offline production pipelines and tooling for model training
  • Scaling and evolving the architecture through an expected ~100x increase in load from deterministic factors
  • Collaborating closely with Product, Data Science, and Machine Learning partners to develop and scale insights products that enable Credit underwriting use cases
  • Mentoring engineers and contributing to a strong, inclusive team culture

### Benefits

  • Vibrant offices in SF, NYC, and Raleigh-Durham—with catered meals, happy hours, and clubs to keep you connected
  • Competitive pay, comprehensive health benefits, and support for fertility, mental health, and parental leave
  • Lifestyle perks including home office stipends, daycare support, and commuting benefits like CitiBike and Lyft- [nice-to-have] Experience working in the credit or lending space
  • Strong experience building and scaling backend products
  • Strong technical leadership skills, including mentoring peers, leading projects and driving architectural decisions
  • Demonstrated success in building and maintaining production systems that serve and support ML models, both ...

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