Staff Machine Learning Engineer, Financial Connections

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