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Hire.Monster

Machine Learning Engineer, Payments ML Accelerator

Seattle; San Francisco; New York City
РазработкаСША

Описание вакансии

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. 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.

What you'll do:

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.

Обязанности

  • 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

Опубликовано: 12.01.2026