Senior Machine Learning Engineer - Credit
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Job Description
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.
Plaid’s Data 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, across different product areas.
We value not only technical know-how, but also creativity, user empathy, and teamwork. You’ll be a machine learning engineer in the Data org, contributing to diverse, high-impact machine learning challenges.
In this role, you’ll focus on designing, building, and deploying scalable ML solutions and systems within the credit environment.
You’ll lead experimentation with new modeling approaches and strategies, collaborate closely with engineers on ingesting signals and productionizing models, and help build the next wave of cash flow based underwriting.
You’ll own AI and machine learning work across the full model lifecycle, from offline training to online serving and monitoring, while helping define the ML roadmap with cross-functional teams.
Responsibilities
Build machine learning systems that empower millions of users through wel