Staff Machine Learning Engineer, Financial Connections

at Stripe

📍 United States
📍 Toronto, Canada
📍 New York City, United States
📍 South San Francisco, United States
📍 Seattle, United States

USD 253,500-380,300 per year

SENIOR

✅ Remote ✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Data Pipelines @ 4 Data Science Deep Learning @ 4 FinTech @ 4 Fraud @ 4 LLM Machine Learning @ 7 Mathematics @ 6 NLP @ 4 PyTorch @ 6 Spark @ 6 Statistics @ 6 TensorFlow @ 6 XGBoost @ 6

Details

Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. The platform connects to thousands of financial institutions and supports use cases including account verification, risk assessment, and personal financial management. The engineering organization builds machine learning systems that transform raw financial data into actionable signals for internal teams and external merchants.

Responsibilities

Design, build, train, evaluate, deploy, and own machine learning models in production for transaction categorization, risk scoring, and data enrichment.
Design and build large-scale machine learning systems operating on diverse financial data from thousands of institutions.
Experiment with and iterate on machine learning models using tools such as PyTorch, TensorFlow, and XGBoost.
Develop pipelines and automated processes to train and evaluate models in offline and online environments.
Integrate machine learning models into production systems and ensure scalability and reliability.
Collaborate with product, data science, and engineering partners to identify opportunities where machine learning can improve outcomes for merchants and consumers.
Engage with the latest machine learning and artificial intelligence developments and transform innovative ideas into productionized solutions.
Mentor engineers and contribute to a strong machine learning engineering culture.

Requirements

10+ years of industry experience building and shipping machine learning systems in production.
Proficiency with machine learning libraries and frameworks such as PyTorch, TensorFlow, XGBoost, and Spark.
Hands-on experience designing, training, and evaluating machine learning models.
Hands-on experience productionizing and deploying models at scale.
Hands-on experience 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, along with an entrepreneurial mindset.

Preferred Qualifications

MS or PhD in machine learning, artificial intelligence, or a related field such as mathematics, physics, statistics, or 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 machine learning systems that solve ambiguous business problems.
Experience with deep learning architectures, including transformers.