Job Description

Who we areAbout the teamFinancial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale — building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants.Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers.What you'll doWe're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem.ResponsibilitiesDesign, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial ConnectionsDesign and build large-scale ML systems that operate on diverse financial data from thousands of institutionsExperiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracyDevelop pipelines and automated processes to train and evaluate models in offline and online environmentsIntegrate ML models into production systems and ensure their scalability and reliabilityCollaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumersEngage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutionsMentor engineers and contribute to a strong ML engineering culture within the teamWho you areWe're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.Minimum requirements10+ years of industry experience building and shipping ML systems in productionProficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as SparkHands-on experience in designing, training, and evaluating machine learning modelsHands-on experience in productionizing and deploying models at scaleHands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasetsStrong collaboration skills and the ability to work across teams and contribute to peers' successAbility to thrive with a high level of autonomy and responsibility and an entrepreneurial mindsetPreferred qualificationsMS or PhD degree in ML/AI or a related field (e.g., math, physics, statistics, computer science)Experience in fintech, open banking, or financial data domainsExperience with NLP, LLMs, or text classification at scaleExperience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data qualityProven track record of building and deploying ML systems that have effectively solved ambiguous business problemsExperience with deep learning architectures, including transformers

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