StripeEngineering Manager, Checkout Optimization
Stripe is hiring an Engineering Manager, Checkout Optimization. Level rates it ; you can apply on Stripe.
Level's read on this job
- Works on AI ●●●○ is how Level rates this role: the daily work is on AI products, without building the model.
- Tools and skills the posting cites include engineering management, full stack and machine learning.
- Level classifies it as a lead role.
- Level lists 720 other open roles at Stripe.
AI in this role
Lead a full-stack product and data engineering team building ML-powered personalized checkout experiences and agentic commerce workflows.
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 Checkout Optimization team is the growth team for Stripe’s checkout UIs. They build high-converting, personalized checkout experiences for buyers and agents, as well as the data platform that powers it.
The team identifies opportunities to improve checkout through rapid product experimentation and delivers optimization at scale through our ML-powered engine. The team also builds the reliable data foundations that enable this work, including experimentation, optimization, and data-informed product decisions across Stripe’s checkout suite.
Over the next 6 to 12 months, the team will focus on:
- Building personalized checkout experiences powered by the ML-based Optimization Engine
- Agentic commerce through optimizing OCS experiences for agent buyers
- Checkout optimization for rapidly growing AI companies
- Improving our data platform, which is critical to leveraging data internally and externally
What you’ll do
Engineering Managers at Stripe are responsible for the success of their team. You’ll be deeply involved in the engineering and design processes as well as coaching, mentoring, and leading the team. You’ll have a deep understanding of how to drive efficient engineering teams and you’ll have a strong user focus. You'll work with engineers on creating technical solutions and communicating effectively across teams and senior leadership.
Responsibilities
- Lead and manage a team of talented full-stack product and data engineers, providing mentorship, guidance, and support to ensure their success
- Understand buyer and agent needs and pain points to prioritize workstreams and deliver high-quality checkout experiences
- Partner with Product, Design, Data Science, Data Engineering, and engineering teams across Stripe to deliver these solutions
- Lead experimentation and optimization work that improves checkout conversion and buyer experiences
- Build and evolve the data platform that enables reliable, self-serve analysis, experimentation, and product decisions across OCS
- Drive high standards for the quality, reliability, and usability of the data products and pipelines that support OCS decision-making
- Recruit top engineering talent in partnership with Stripe’s recruiting team to build a diverse and high-performing team
- Drive the execution of projects, overseeing the entire development lifecycle from planning to delivery, while maintaining high standards of quality and timely completion
- Provide hands-on technical leadership, including architecture and design, vision, direction, requirements setting, and incident response processes for your team
- Set the team’s direction, from north star vision to goals, OKRs, and roadmap
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
- 5+ years of engineering management experience, directly managing and growing teams of 5+ engineers focused on building and shipping products at scale
- 10+ years of full-time software development experience, with a strong background in backend development
- Experience working across geographies
- Experience leading growth or product teams that have used experimentation to drive product usage and other metrics
- Experience building extensible, leveraged software solutions that scale to different kinds of user needs, with the ability to empathize with users and advocate for excellent user experiences
Preferred qualifications
- Previous experience working in payments and on payments technology
- Previous experience leading a growth team
- Previous experience leading a data engineering team
How we rate this
Engineering Manager, Checkout Optimization at Stripe rates 60 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● 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 engineering management was part of your work. What did you do?
- Tell me about a project where full stack was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where experimentation was part of your work. What did you do?
- Describe a typical day in a role like this one: which parts run through AI directly?
Adapt your resume
- List these exact terms on your resume: Engineering Management, Full Stack, Machine learning, and Experimentation. 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.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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