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Machine Learning Engineer

Stripe•South San Francisco, CA
Full-time3-7
👁️ 4 views•📝 0 applications•Posted 8/18/2026•Expires 10/22/2026
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Job Description

Who we are About Stripe Stripe, LLC. 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. What you’ll do Responsibilities Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints. Design systems to speed up the time from idea to deployment of new models. Experiment and iterate on ML models (using tools including PyTorch and TensorFlow) to achieve key business goals and drive efficiency. Develop pipelines and automated processes to train and evaluate models in offline and online environments. Integrate ML models into production systems and ensure their scalability and reliability. Collaborate with product and strategy partners to propose, prioritize, and implement new product features. Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions. Who you are Minimum requirements Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of experience in Building and shipping ML systems in production. Must have two (2) years of expe

Required Skills

Machine LearningPyTorchTensorFlowML SystemsProduction MLModel DeploymentPythonComputer ScienceMathematicsStatisticsPhysicsUnderwritingPortfolio ManagementRisk ModelingRegulatory ComplianceAutomated PipelinesOnline/Offline Model EvaluationScalabilityReliabilityProduct CollaborationFeature Implementation

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