Engineering Manager, Machine Learning - Fraud Detection

About the team

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

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

• 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

Minimum requirements

• At least 3 years of engineering management experience
• Prior experience as a Machine Learning Engineer or equivalent

Preferred qualifications

• 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