Engineering Manager, Machine Learning - Credit Risk

at Stripe

📍 Canada
📍 United States
📍 Toronto, Canada
📍 Atlanta, United States
📍 Chicago, United States
📍 New York City, United States
📍 South San Francisco, United States
📍 Seattle, United States

USD 258,600-387,800 per year

SENIOR

✅ Remote ✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Data Science @ 4 Engineering Management Fraud @ 4 Machine Learning @ 6 Payments

Details

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies use Stripe to accept payments, grow revenue, and accelerate new business opportunities.

The Credit Risk team develops intelligent systems that help Stripe identify high-risk accounts, minimize credit losses, and improve profitability. The team builds machine learning models and systems for credit-risk products and works closely with Product, Data Science, Credit Strategy, Operations, and other engineering teams.

Responsibilities

Set and execute the strategy for detecting and mitigating credit risk through machine learning.
Own outcomes related to credit losses, profitability, detection quality, and user experience.
Lead the design and delivery of reliable machine learning models, services, and decision systems.
Translate advances in machine learning into practical capabilities that support business goals.
Partner with Product, Data Science, Credit Strategy, Operations, and engineering teams to define priorities and deliver cross-functional programs.
Recruit, hire, and develop machine learning engineers while building an inclusive and effective team.
Contribute to broader engineering and machine learning initiatives as a member of Stripe's engineering management team.

Requirements

Minimum Requirements

3+ years of experience managing engineers who build and operate production machine learning systems.
Experience applying machine learning to complex, real-world problems and leading the technical delivery of models and supporting systems.
Experience setting strategy and working across engineering, product, data science, operations, and business teams to deliver measurable outcomes.
Experience recruiting, managing, and developing engineers in a fast-moving environment with significant autonomy.

Preferred Qualifications

Experience with credit risk, fraud detection, financial risk, trust and safety, or another domain involving decisions under uncertainty.
Experience balancing risk reduction with customer or user experience.
Experience building machine learning systems that support high-stakes, time-sensitive decisions at scale.
Experience setting a multi-year technical direction while delivering progress through quarterly plans.
Experience managing geographically distributed teams.