ML Engineer: Income Insights for Smart Lending

Plaid Inc.; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

Plaid Inc. is hiring a Machine Learning Engineer for the Credit team in New York. You will design, build, and deploy scalable ML solutions that support Income Insights and cash-flow-based underwriting.

You will work across the full model lifecycle—from experimentation to production serving and monitoring—collaborating with engineers and product teams to deliver reliable systems. You will develop AI/ML models, experiment with credit-use case approaches, and ensure production readiness while

Qualifications

• 2+ years of experience training and serving ML models in production.
• Experience with data-intensive backend applications in distributed systems.
• Ability to code and iterate with Python, Spark, Jupyter notebooks, and standard ML libraries.

Responsibilities

• Develop and productionize AI and machine learning models, from offline training through online serving and ongoing monitoring.
• Experiment with modeling approaches for credit use cases, balancing performance with lending requirements.
• Partner with engineering to ingest signals and build systems to bring models into production.
• Contribute to 0-to-1 problems and scaling existing solutions.
• Collaborate across Plaid to help define the ML roadmap.
• Dive into data to guide day-to-day decisions.

Skills

AI/ML modeling

Production ML

Python

Spark

Data pipelines

Cross-functional collaboration

Ownership

Communication

Education

Master's degree in CS/Math/Engineering

Tools

Python

Spark

Jupyter

ML libraries

Job description

Plaid Inc. is hiring a Machine Learning Engineer for the Credit team in New York. You will design, build, and deploy scalable ML solutions that support Income Insights and cash-flow-based underwriting.

You will work across the full model lifecycle—from experimentation to production serving and monitoring—collaborating with engineers and product teams to deliver reliable systems. You will develop AI/ML models, experiment with credit-use case approaches, and ensure production readiness while