Plaid - Machine Learning Engineer
Requirements
• 5+ years of experience in machine learning, including deploying machine learning models into real-world, customer facing systems. • High agency and creativity; experience identifying, defining, and proposing high impact machine learning opportunities. • Ability to analyze large and complex financial datasets to derive insights. • Advanced degree or equivalent work experience in Statistics, Economics, Mathematics, Data Science, or a related field. • Proficiency in SQL, Python, and data visualization/analysis tool. • Ability to clearly communicate complex technical systems and decision making. • Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Responsibilities
• Opportunity to shape Plaid’s future as a company where intelligence products are a core value proposition. • Dive into one of the most unique datasets available in the industry and shape the strategy to leverage its value. • Work across many different areas and learn deeply about the entire Plaid product suite • Build products that empower millions of people to achieve financial freedom and opportunity. • Work closely with customers to ensure products meet their needs and demonstrate true impact.
Benefits
• $211,680 – $272,160 • Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans. • 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. • The Embedded Insights team supports Plaid’s mission to build a world-class suite of intelligence products. We identify the best opportunities to use machine learning in Plaid products, prove out those opportunities, and collaborate with cross-functional partners to turn them into real world production systems. • As a Machine Learning Engineer on the Embedded Insights team, you will drive machine learning initiatives from concept to production, working across the full model development lifecycle. You will leverage Plaid’s unique datasets to identify high-impact opportunities for machine learning, develop proofs of concept to validate new approaches, and build MVP solutions that demonstrate customer value. Partnering closely with product managers, engineers, and other cross-functional stakeholders, you will embed within product teams to translate successful prototypes into scalable, customer-facing products. As solutions gain traction, you will help expand their reach by optimizing models for new use cases, improving system scalability, and incorporating customer feedback gathered before and after launch. You will also be responsible for maintaining and enhancing existing machine learning systems through feature development, retraining strategies, and robust monitoring frameworks, including metrics, alerts, and dashboards that ensure model performance, reliability, and long-term health.
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