Machine Learning Engineering Manager, ML Foundation

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 goal of our team is to turn the universe of businesses into the best source of information available. We’re leveraging public data from Stripe's current businesses, utilizing their website information to conduct insightful analyses—like determining what industry a business operates in or generating tailored product descriptions. By being part of this team, you’ll play a vital role in shaping tools that make a real difference in the lives of our users, allowing them to streamline their processes and drive their success.

What you’ll do

Responsibilities

Driving an ambitious vision for AI/ML that benefits our users
Setting the technical & process direction for the team based on business goals
Brainstorm and coordinate product integrations with partner teams
Proposing new ideas and building prototypes
Be an integral part of a larger ML community internally & externally
Hire & develop a world-class team to deliver high-quality ML systems.
Coach engineers to help them grow in their careers and maintain a high bar

Who you are

We are looking for ML Engineering Managers who are passionate about using ML to improve products and delight customers. You have experience leading teams that develop streaming feature pipelines, build ML models, and deploy them to production, even if it involves making substantial changes to backend code. You are comfortable with ambiguity, love to take initiative, and have a bias towards action.

Minimum requirements

Have at least 4 years of experience managing ML teams
Lead by example in high-growth, high-impact, ambiguous environments
Have experience building & shipping ML systems.
Enjoy measuring & improving user experience
Hold yourself and others to a high bar when working with production systems.
Thrive in a collaborative cross-functional environment

Preferred qualifications

Experience in shipping LLM & RAG systems

Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.

The annual US base salary range for this role is $253,900 - $380,900. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process.

Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.

Office locations

New York

Team

Machine Learning

Job type

Full time

Apply for this role

At Stripe, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us.

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