Machine Learning Engineer, Payments 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.
What this role actually needs.
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. 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 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 Company context: Stripe builds financial infrastructure for internet businesses, with strong hiring across platform, product, data, and AI-adjacent teams.
Day-to-day expectations
Stripe lists these responsibilities for the Machine Learning Engineer, Payments ML Accelerator role.
- 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
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 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
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Source: stripe.com · Source ID: 7079044 · Confidence: 94/100 · Last checked: May 7, 2026
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