Experienced Machine Learning Modeler - Payments

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. #LI-Hybrid

Plaid’s Machine Learning team is building models that improve how millions of users understand and grow their financial lives. We're looking for machine learning engineers with experience applying state-of-the-art machine learning and modeling techniques -- including natural language processing, anomaly detection, optimization, and time series forecasting -- toward different product areas. We value not only technical know-how, but also creativity, user empathy, and teamwork.

You will be a machine learning modeler on the payments team focused on building and maintaining machine learning models to power products and platforms.

Responsibilities

• Hands-on develop, productionize, and operate Machine Learning models and pipelines to improve a diverse range of Plaid products.
• Continuously proposing and developing new features to improve the AI/ML model performance.
• Working with the ML infrastructure team to improve ML infrastructure that powers the end-to-end ML development lifecycle.
• Debugging ML production issues and ensuring stable model serving.
• Work collaboratively with cross-functional partners to identify opportunities for business impact, understand, refine, and prioritize requirements for AI/ML models, drive engineering decisions, and quantify impact.

Qualifications

• 5+ years of engineering experience.
• Proficiency in machine learning algorithms and solid understanding of mathematics and statistics.
• Experience in developing end to end data systems/products and productionizing AI/ML models.
• Experience in well-known big data processing infrastructures, like Spark, Airflow, DBT, Hive, Presto, and etc.
• Ability to architect software and ML systems at scale.
• Solid software engineer skill in complex and multi-language systems. Code fluency in Python.
• Experience in working with product, design, and backend engineering.