Staff Machine Learning Engineer, Financial Connections
Stripe · New York
About this role
**Staff Machine Learning Engineer, Financial Connections** **About the Team** Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. The team focuses on delivering high-quality, enriched bank data at scale—building ML systems that transform raw financial data into actionable signals for internal Stripe teams and external merchants. **What You'll Do** • Design, build, train, evaluate, deploy, and own ML models in production for transaction categorization, risk scoring, and data enrichment • Design and build large-scale ML systems operating on diverse financial data from thousands of institutions • Experiment and iterate on ML models using PyTorch, TensorFlow, XGBoost to achieve business goals • Develop pipelines and automated processes for training and evaluating models in offline and online environments • Integrate ML models into production systems ensuring scalability and reliability • Collaborate with product, data science, and engineering partners to identify ML opportunities • Engage with latest ML/AI developments and transform innovative ideas into productionized solutions • Mentor engineers and contribute to strong ML engineering culture **Minimum Requirements** • 10+ years of industry experience building and shipping ML systems in production • Proficiency with PyTorch, TensorFlow, XGBoost, and Spark • Hands-on experience designing, training, and evaluating machine learning models • Hands-on experience productionizing and deploying models at scale • Experience orchestrating data pipelines and leveraging large-scale datasets • Strong collaboration skills and ability to work across teams • Ability to thrive with high autonomy and entrepreneurial mindset **Preferred Qualifications** • MS or PhD in ML/AI or related field (math, physics, statistics, computer science) • Experience in fintech, open banking, or financial data domains • Experience with NLP, LLMs, or text classification at scale • Experience in adversarial or noisy-data domains (fraud detection, risk modeling, data quality) • Proven track record building ML systems that solved ambiguous business problems • Experience with deep learning architectures including transformers
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