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.

About the team

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.

Responsibilities

• 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

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

• 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

Preferred qualifications

• 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 niche and upcoming ML solutions

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