Job description

Who we are About the team Financial 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 do We'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. Responsibilities Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy 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, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions Mentor engineers and contribute to a strong ML engineering culture within the team Who you are We'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 requirements 10+ years of industry experience building and shipping ML systems in production Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark Hands-on experience in designing, training, and evaluating machine learning models Hands-on experience in productionizing and deploying models at scale Hands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasets Strong collaboration skills and the ability to work across teams and contribute to peers' success Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset Preferred qualifications MS 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 domains Experience with NLP, LLMs, or text classification at scale Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems Experience with deep learning architectures, including transformers

Requirements

Must-haves

Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections
Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions
Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy
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, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers
Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions
Mentor engineers and contribute to a strong ML engineering culture within the team
10+ years of industry experience building and shipping ML systems in production
Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark
Hands-on experience in designing, training, and evaluating machine learning models
Hands-on experience in productionizing and deploying models at scale
Hands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasets
Strong collaboration skills and the ability to work across teams and contribute to peers' success
Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset

Nice-to-haves

We'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.
MS 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 domains
Experience with NLP, LLMs, or text classification at scale
Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality
Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
Experience with deep learning architectures, including transformers

Application prep

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Keywords to mirror

mlmachine learningai

Platform: greenhouse
Effort: quick