Engineering Manager, Machine Learning - Credit Risk

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

Benefits offered by this job

Hybrid/Remote work options

Job summary

Stripe seeks an Engineering Manager to lead a team of machine learning engineers focused on credit risk within its Risk & Financial Crimes organization. This role blends people leadership with technical direction, guiding ML initiatives that inform risk decisions and financial-crimes objectives across Stripe.

The role involves shaping the technical roadmap for ML systems, partnering with product, data science, and risk stakeholders, and overseeing the full lifecycle of production ML systems from

Qualifications

• Experience leading ML 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.

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

Skills

ML leadership

Applied ML

Cross-functional collaboration

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