Machine Learning Engineering Manager, ML Foundation
: Job Details :

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 theinternet, 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 newly formed Stripe Assistant team aims to build an assistant for all merchants who use Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include customizing the Stripe landing page to suggest bespoke integrations, allowing users to command the Stripe API in natural language, and resolving user issues automatically. We are developing RAG based systems on the latest LLMs as well as fine-tuning our own models. We're an end-to-end team going from ideas to models to shipping in production.

What you'll doResponsibilities

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 Qualifications

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 $224,300 - $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: Seattle, New York, or South San Francisco HQ

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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