Stripe is hiring a Machine Learning Engineering Manager - Fraud Detection in 8535 Risk Credit & Fraud — N/A. The overview below is synthesized from the employer posting on stripe.com: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
About Stripe
Company context from the listing:
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.
About the team
Company context from the listing:
Risk Detection is focused on providing a delightful experience for our merchants and minimizing friction, while ensuring the safety of our users in the financial ecosystem. Whenever the Risk team takes action on an account, we work to notify Merchants and provide clear status on what’s happening, enable guided workflows for resolving their issues, and redefine our overall Risk processes to make them as smooth as possible for good merchants.
What you’ll do
Day-to-day scope for the Machine Learning Engineering Manager - Fraud Detection as described in the posting:
We’re looking for an engineering leader to lead and grow a strong team of engineers, build relationships with customers internally and externally, and champion our vision of making Stripe’s risk management a feature that attracts and retains merchants, and becomes a product differentiator. This is an exciting opportunity to partner with teams across Stripe to build the best merchant experience, and contribute directly to Stripe’s growth.
Responsibilities
Day-to-day scope for the Machine Learning Engineering Manager - Fraud Detection as described in the posting:
- Support the team in delivering a high level of technical quality and impact via APIs, user-facing experiences, services, and systems
- Recruit, hire, scale, and develop an amazing team of engineers
- Executing cross-functionally with leadership, product teams, infra teams & risk strategists
- Be actively involved in strategic direction and platform decisions that impact all of Stripe and Stripe customers
Who you are
Experience and skills the team lists as required:
The listing frames the Machine Learning Engineering Manager - Fraud Detection at Stripe as a hands-on product role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
Minimum requirements
Experience and skills the team lists as required:
- At least 3 years of engineering management experience
- Prior experience as a Machine Learning Engineer or equivalent
Preferred qualifications
Experience and skills the team lists as required:
- You have managed teams that can collaborate with product teams, respond rapidly to customer needs along with building technology and capabilities that are strategic and foundational in nature
- Enjoy designing, measuring & improving user experience
- You are empathetic to customer needs but visionary enough to not just deliver a faster horse
- You are comfortable planning in quarters, and can set a vision for several years
- You are comfortable working with geographically distributed teams and remote workers
Build a standout application
For the Machine Learning Engineering Manager - Fraud Detection at Stripe, reviewers look for concise evidence over buzzwords. Mirror the language of the posting sparingly, quantify support or delivery outcomes, and show how you handled ambiguity, time-zone collaboration and user empathy. Keep your resume to impact, keep your cover note to one page, and link to artifacts — tickets resolved, docs shipped, dashboards owned — that prove you can operate in a fast-moving Web3 team. Prepare to discuss a time you turned a confusing user report into a clear fix and how you measure quality in support and operations.
Web3 hiring values reliability: on-time follow-through, clear writing, and a track record of improving runbooks and tooling. Treat the application as a work sample. For interviews, be ready to walk through how you prioritize across time zones, handle a difficult user, and decide when to escalate versus resolve directly. Show how you document decisions so the next teammate benefits.
In a distributed Web3 org, trust builds through written clarity. Use the cover note to demonstrate it.