Staff Machine Learning Engineer, Financial Connections
Remote • New York
Posted 10d ago
Job Location
New York
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
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.
Responsibilities
- 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 that operate on diverse financial data.
- Experiment and iterate on ML models using tools like PyTorch, TensorFlow, and XGBoost to achieve business goals.
- Develop pipelines and automated processes for offline and online model training and evaluation.
- Integrate ML models into production systems, ensuring scalability and reliability.
- Collaborate with product, data science, and engineering partners to identify ML opportunities.
- Engage with the latest ML/AI developments to transform innovative ideas into productionized solutions.
- Mentor engineers and contribute to a strong ML engineering culture.
Requirements
- 10+ years of industry experience building and shipping ML systems in production.
- Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, and 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 ability to work across teams.
- Ability to thrive with high autonomy and responsibility, and an entrepreneurial mindset.
- MS or PhD degree in ML/AI or a related field (preferred).
- Experience in fintech, open banking, or financial data domains (preferred).
- Experience with NLP, LLMs, or text classification at scale (preferred).
- Experience in adversarial or noisy-data domains (preferred).
- Proven track record of building and deploying ML systems to solve ambiguous business problems (preferred).
- Experience with deep learning architectures, including transformers (preferred).