Machine Learning Engineer, ML Accelerator

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.

The Payments ML Accelerator team is developing foundational ML capabilities that drive innovation across Stripe's payment products. We build deep learning models that tackle Stripe's most complex payment challenges - from fraud detection to authorization optimization - and deliver measurable business impact. Our work combines advanced ML techniques with large-scale data infrastructure to enable rapid experimentation and seamless deployment of AI-powered solutions.

As a central ML innovation hub, we work closely with product teams to identify high-impact opportunities and implement scalable solutions that can be leveraged across the organization.

As a machine learning engineer on our team, you’ll develop advanced ML solutions that directly impact Stripe’s payment products and core business metrics. Your role will span the entire ML lifecycle, from research and experimentation to production deployment.

You’ll work on high-leverage problems that require innovation in modeling, optimization, and system design. Where possible, you’ll look beyond point solutions - designing approaches and architectures that are reusable, extensible, and serve as foundation models for future capabilities.

The role demands strong technical judgment, deep knowledge of modern ML methods, and the ability to translate ideas into systems that deliver measurable impact. You’ll partner with product and engineering teams to identify opportunities where ML can move the needle today while setting Stripe up for long-term success.

• Design and deploy deep learning architectures and foundation models to address problems across key payment entities such as merchants, issuers, or customers
• Identify high-impact opportunities, and drive the long-term ML roadmap through well-scoped high-leverage initiatives
• Architect generalizable ML workflows to enable rapid scaling and optimized online performance
• Deploy ML models online and ensure operational stability
• Experiment with advanced ML solutions in the industry and ideate on product applications
• Explore cutting-edge ML techniques and evaluate their potential to solve business problems
• Work closely with ML infrastructure teams to shape new platform capabilities

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 7 years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production
• Proficient in Python, Scala, and Spark
• Proficient in deep learning and LLM/foundation models

• MS/PhD degree in quantitative field or ML/AI (e.g. computer science, math, physics, statistics)
• Knowledge about how to manipulate data to perform analysis, including querying data, defining metrics, or slicing and dicing data to evaluate a hypothesis
• Experience evaluating niece and upcoming ML solutions

This role is available either in an office or a remote location (typically, 35+ miles or 56+ km from a Stripe office).

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.

A remote…