Staff Software Engineer - Credit Insights
We believe Plaid has the power to be the next-gen Credit Bureau - supporting large scale adoption of cash flow into the credit underwriting process.
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.
Responsibilities:
- 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.
Qualifications:
- 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 in online and offline settings - as well as working closely with data science or ML teams
- Experience collaborating with cross-functional stakeholders and with teams and leaders across the engineering function
- [nice-to-have] Experience working in the credit or lending space