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 ...