Engineering Manager

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

Stripe processes over $1T in payments volume per year, which is roughly 1% of the world’s GDP, for millions of customers from startups to enterprises. The tremendous amount of data makes Stripe one of the best places to do machine learning. While being an integral part of almost every product line at Stripe (e.g., Payments, Radar, Capital, Billing, etc.), ML is still in its early days in realizing its full potential at Stripe. 

The Feature Production team as part of ML Features under ML Foundation is responsible for making deploying ML Features fast, reliable and cost efficient, reducing charge path latency by optimizing feature compute and serving time, make feature compute cheaper by sharing features during runtime, making Shepherd scale horizontally, creating visibility for resource usage for our users etc.  We work closely with ML engineers, data scientists, and platform infrastructure teams to build the powerful, flexible, and user-friendly systems that substantially increase ML velocity across the company.

What you’ll do

You will have the opportunity to shape the future of doing ML efficiently and scalably at Stripe. You will help define the long-term strategy and lead the team in building the next generation of ML foundations that power most if not all of Stripe’s products. 

Responsibilities

• Hire, lead and manage a team of talented engineers on the team, providing mentorship, guidance, and support to ensure their success.
• Collaborate with cross-functional teams, including senior leadership, data platform, infrastructure, data science, machine learning engineering, and other business orgs to understand user needs and translate them into technical solutions.
• Define the vision and roadmap for increasing ML feature development and release velocity at Stripe, aligning it with business objectives and industry best practices.
• Drive the execution of projects, overseeing the entire development lifecycle from planning to delivery, while maintaining high standards of quality and timely completion. 
• Foster a collaborative and inclusive work environment, promoting innovation, knowledge sharing, and continuous improvement within the team.
• Stay up-to-date with emerging technologies, industry trends, and advancements in ML practices to identify opportunities for improvement and innovation. 
• Communicate effectively with stakeholders, providing regular updates on project status, progress, and any potential risks or challenges. 

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

• 10+ years of software development experience and 3+ years of engineering management experience building infrastructure related products
• Proven track record of building and operating large scale, highly available and low latency systems
• Experience working in highly cross-functional organizations
• The ability to thrive on a high level of autonomy and responsibility
• The desire to encourage a healthy, inclusive work environment that’s both supportive and challenging
• Clear and persuasive writing and in-person communication

Preferred qualifications

• Experience with ML Feature compute and serving 
• Experience in building large-scale infrastructure for machine learning use cases
• Familiarity with cloud services (e.g., AWS) and production monitoring tools
• Comfortable working with geographically distributed teams