Staff Machine Learning Engineer

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Staff Machine Learning Engineer in USA

Stripe
Full time
Email

USA
08/25/2026
Report

New York, NY

Responsibilities

Design, build, train, evaluate, deploy, and own production ML models for transaction categorization, risk scoring, and financial data enrichment.
Build large-scale ML systems using diverse financial data from thousands of institutions.
Experiment with ML frameworks and models to improve data quality, accuracy, and business outcomes.
Develop automated pipelines for training and evaluating models in offline and online environments.
Integrate ML models into production systems while ensuring scalability and reliability.
Collaborate with product, data science, and engineering partners to identify ML opportunities.
Evaluate emerging ML and AI developments and productionize promising solutions.
Mentor engineers and contribute to the team’s ML engineering culture.

Requirements

10+ years of industry experience building and shipping ML systems in production.
Proficiency with 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 leveraging large-scale datasets efficiently.
Strong collaboration skills and the ability to work across teams and support peers’ success.
Ability to work autonomously with substantial responsibility and an entrepreneurial mindset.
Preferred: MS or PhD in ML/AI or a related field such as mathematics, physics, statistics, or computer science.
Preferred: Experience in fintech, open banking, financial data, NLP, LLMs, text classification, fraud detection, risk modeling, data quality, or ambiguous business problem-solving.
Preferred: Experience with deep learning architectures, including transformers.

Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities.

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