Machine Learning Engineer, Stripe Assistant

Who we areAbout 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

Our Applied ML team aims to reform how our users interact with Stripe. We are doing so by (a) automating the easy tasks, and (b) assisting our users in the difficult tasks. Some examples include helping our users resolve issues with Stripe faster or making it easier for our users to sign up and navigate Stripe. We are using 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. You can learn more about our team’s work from this recent talk.

What you’ll do

As a machine learning engineer, you will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to production. You will also have the opportunity to contribute to and influence ML architecture at Stripe as well as be a part of a larger ML community.

Responsibilities

Our team operates fluidly and here are some problems you may tackle:

• How do we evaluate a system offline & online?
• How do we improve performance to match (and beat) humans?
• How do we ensure model quality doesn’t degrade online?
• Does fine-tuning an LLM give us better performance?
• What are the right OSS and in-house platforms we should invest in?

Who you are

We are looking for ML Engineers who are passionate about using ML to improve products and delight customers. You have experience developing streaming feature pipelines, building ML models, and deploying 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 3 years of experience shipping ML systems in production
• Hold yourself and others to a high bar when working with production systems
• Take pride in taking ownership and driving projects to business impact
• Thrive in a collaborative environment

Preferred qualifications

• 5+ years of experience in full time software development roles
• Experience shipping LLM integrations to user products with high quality
• Experience operating in highly ambiguous environments
• Knowledge about driving a hypothesis from data