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

This role is available either in an office or a remote location (35+ miles or 56+ km from a Stripe office).

In-office expectations

Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.

Working remotely at Stripe

A remote location is defined as being 35 miles (56 kilometers) or more from one of our offices. While you would be welcome to come into the office for team/business meetings, on-sites, meet-ups, and events, our expectation is you would regularly work from home rather than a Stripe office. Stripe does not cover the cost of relocating to a remote location. We encourage you to apply for roles that match the location where you currently live or plan to live.

The annual US base salary range for this role is $258,600 - $387,800. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process.

Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.

Please find our California applicant personal information notice here.

The application window will remain open for 100 days after the Job Post is published. However, this opportunity will remain open based on the needs of the business, which may cause the application window to close before or after the 100-day mark.