Jobs › Machine Learning Engineer - (Payment Risk/Fraud) - Embedded Insights
Machine Learning Engineer - (Payment Risk/Fraud) - Embedded Insights
Role at a glance
- Category
- Risk
- Work arrangement
- On-site
- Location
- San Francisco
- Posted
- Jul 3, 2026
Plaid is hiring a Machine Learning Engineer - (Payment Risk/Fraud) - Embedded Insights in San Francisco. This is a Risk role in the governance, risk, and compliance field. Review the full details below and apply directly with Plaid.
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. 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
Full responsibilities and requirements are on Plaid's application page.
Apply for this role →Location and market context
This role is based in San Francisco on-site. Local candidates benefit from being close to Plaid's teams and regional hiring market. Confirm the exact in-office expectation and any relocation support with the employer.
About risk management roles
Risk roles own the methodology for identifying, assessing, and escalating enterprise, operational, and technology risk. Second-line teams set risk appetite and challenge the first line. Roles like this one are typically evaluated against frameworks such as enterprise and operational risk frameworks, NIST AI RMF, and risk-appetite and escalation practices.
How to position yourself for this risk management role
Strong candidates emphasize risk assessment methodology, appetite and escalation, cross-functional partnership, and clear reporting to senior leadership and the board. In your resume and outreach, tie your experience to how Plaid would apply enterprise and operational risk frameworks, NIST AI RMF, and risk-appetite and escalation practices, and lead with concrete outcomes rather than duties.
Similar GRC roles
More GRC jobs: All GRC roles · Browse by category & location