StripeEngineering Manager, Machine Learning - Credit Risk
Stripe is hiring an Engineering Manager, Machine Learning - Credit Risk. Level rates it ; you can apply on Stripe.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions Machine learning, credit risk and engineering management among its tools and skills.
- Level classes it as a lead role.
- Stripe has 714 other open roles on Level.
AI in this role
Lead the Credit Risk machine learning engineering team at Stripe to build intelligent fraud and risk detection systems.
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
How we rate this
Engineering Manager, Machine Learning - Credit Risk at Stripe rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where credit risk was part of your work. What did you do?
- Tell me about a project where engineering management was part of your work. What did you do?
- Tell me about a project where python was part of your work. What did you do?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: Machine learning, Credit Risk, Engineering Management, and Python. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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