Engineering Manager, Machine Learning - Credit Risk

Location: N/A

Department: 8535 Risk Credit & Fraud

Engineering Manager, Machine Learning Credit Risk

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies from the world’s largest enterprises to the most ambitious startups use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Credit Risk team develops intelligent systems that help Stripe identify high-risk accounts, minimize credit losses, and improve profitability. Credit risk is a complex machine learning problem that requires us to distinguish emerging risk from healthy business activity while giving legitimate users a clear and reliable experience.

Our team consists of machine learning engineers who build models and systems used across Stripe’s credit-risk products. We work closely with partners in Product, Data Science, Credit Strategy, Operations, and other engineering teams. Together, we help stakeholders make informed decisions and support sustainable growth wherever credit risk affects Stripe’s products.

What you’ll do

We’re looking for an engineering manager to lead the Credit Risk team and shape how Stripe uses machine learning to manage credit risk at scale. You’ll set the team’s technical and product direction, connect advances in machine learning to measurable business outcomes, and help engineers deliver reliable systems that balance loss prevention with the user experience.

You’ll work across engineering, product, data science, and risk to identify the highest-impact opportunities and turn them into a focused roadmap. You’ll also hire and develop engineers, strengthen the team’s technical practices, and contribute to machine learning and engineering leadership across Stripe.

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 the 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 the team’s 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

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

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