Engineering Manager, Machine Learning - Credit Risk

stripe.com; United States; Hybrid; USD 180,000 - 240,000; Full time

Job summary

Stripe is seeking an Engineering Manager to lead a team of machine learning engineers focused on credit risk within the Risk & Financial Crimes organization. You will blend people leadership with technical direction to advance ML initiatives that inform risk decisions.

This full-time role offers hybrid and remote options across Canada and the United States, with hubs in Toronto, Atlanta, Chicago, New York, Seattle, and the South San Francisco area.

Qualifications

• Experience leading or managing ML engineering teams.
• Applied ML background with exposure to credit risk, fraud, or financial-crimes.
• Familiarity with production ML systems including evaluation, monitoring, and model lifecycle management.
• Ability to collaborate across risk, product, and engineering teams.

Responsibilities

• Manage and develop a team of ML engineers focused on credit risk modeling and decisioning.
• Shape the technical roadmap for ML systems that support risk priorities.
• Partner with product, data science, and risk stakeholders to align on objectives and outcomes.
• Oversee the full lifecycle of production ML systems, from development through ongoing monitoring.
• Coach engineers on experimentation, model quality, and shipping reliable ML-powered products.

Skills

People leadership

Applied ML

Production ML

Cross-functional collaboration

Credit risk experience

Job description

Role overview

The Engineering Manager will lead a team of machine learning engineers focused on credit risk within the Risk & Financial Crimes organization. The role combines people leadership with technical direction, guiding ML initiatives that inform risk-related decisions and financial-crimes objectives across the business.

Responsibilities

• Manage and develop a team of machine learning engineers working on credit risk modeling and decisioning
• Shape the technical roadmap for ML systems that support risk and financial-crimes priorities
• Partner with product, data science, and risk stakeholders to align on objectives and outcomes
• Oversee the full lifecycle of production ML systems, from development through ongoing monitoring
• Coach engineers on experimentation, model quality, and shipping reliable ML-powered products

Requirements

• Experience leading or managing machine learning engineering teams
• Applied ML background, ideally with exposure to credit risk, fraud detection, or financial-crimes work
• Familiarity with production ML systems, including evaluation, monitoring, and model lifecycle management
• Ability to collaborate effectively across risk, product, and engineering organizations
• Eligibility to work remotely from Canada or the United States

Benefits and work setup

• Full-time position
• Hybrid and remote options across Canada and the United States, with hubs in Toronto, Atlanta, Chicago, New York, Seattle, and the South San Francisco area