Staff Machine Learning Engineer, Financial Connections
Stripe Open · verified Oct 22, 2026 At a glance
Senior Machine Learning Engineering role at Stripe. New York · no salary stated · we estimate $194,875–$271,250.
Stripe has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and run production models that improve transaction categorization, risk scoring and financial data enrichment
- Create machine learning systems for financial information arriving from thousands of institutions
- Test and refine models, then build pipelines to train and assess them in offline and live settings
- Connect models to production services and keep them reliable as they scale
- Work with product, data science and engineering peers to find useful ML applications, and mentor engineers
What you bring
- At least ten years building machine learning systems and bringing them into production
- Practical skill with ML frameworks such as PyTorch, TensorFlow or XGBoost, plus Spark
- Hands-on experience designing, training and assessing models, deploying them at scale, and orchestrating data pipelines
Who this fits
This senior role suits an experienced ML engineer comfortable owning work with substantial autonomy and collaborating across teams. The job includes mentoring other engineers. It is based in New York.
From the employer
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