This role pays more than
85%
of similar roles. Most pay
$202,800–$254,750
— the shaded band above.
At the midpoint, this role pays about
$265k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Stripe
Stripe is a financial infrastructure platform for internet businesses, providing payment processing, billing, fraud prevention, and banking-as-a-service APIs to businesses of all sizes globally. Industry: Payments Infrastructure & Financial Technology
Stripe currently has
145 open roles
on FindRole.
Listed pay typically runs
$203,600–$286,800
across 142 roles with salary data.
Machine Learning Engineer, Link joins the Link Fraud and Auth team to build and operate models and risk decisioning systems that protect consumers and merchants from fraud while maximizing authorization rates for legitimate users. This role involves managing the full machine learning lifecycle, including analyzing fraud patterns, designing data pipelines, developing features, and deploying models into production. The engineer will work with large-scale datasets to investigate emerging threats and develop pragmatic solutions like tree-based models for real-time risk decisioning. Key technical requirements include over six years of experience building production models using Python, SQL, Spark, and XGBoost. The role focuses on the payment infrastructure domain, specifically addressing fraud detection, account security, and payment performance within a digital wallet ecosystem to ensure secure transactions across various products like agentic wallets and Instant Bank Payments.
What does a Machine Learning Engineer earn in New York?
Median $244450 from 60 postings across 21 companies.
Build, train, evaluate, deploy, and own machine learning models to detect fraud and abuse across Link.
Analyze large-scale datasets to investigate emerging threats and identify opportunities for improved payment performance.
Develop pragmatic machine learning solutions, including tree-based models, for real-time risk decisioning.
Design data pipelines, features, evaluation methods, and monitoring systems to support reliable production models.
Build and improve risk decisioning systems that integrate with Stripe’s broader payments stack.
Solve ambiguous problems by leading the process from initial analysis and technical design through implementation and measurement.
Translate model improvements into durable product outcomes by partnering with Engineering, Product, and Data Science teams.
What we're looking for
6+ years of industry experience building and shipping machine learning models in production.
Strong programming skills in Python and experience with common data and machine learning tools like SQL, Spark, and XGBoost.
Strong knowledge of production machine learning systems including data pipelines, feature development, model evaluation, deployment, monitoring, and iteration.
Experience working with large and complex datasets while applying data analysis, statistics, and experimentation fundamentals.
Demonstrated ability to take an open-ended business problem and own the solution through production.
Strong judgment in selecting practical modeling approaches and evaluating tradeoffs among performance, complexity, latency, and impact.
Strong collaboration skills and the ability to work across teams.
Experience in fraud detection, risk modeling, payments, or other adversarial domains (preferred); experience with real-time systems (preferred).
Machine Learning
Python
R
Java
TensorFlow
PyTorch
scikit-learn
Distributed Systems
APIs
Data Pipelines
Statistics
Probability Theory
Optimization
Copilot
Claude Code