below marketmost similar roles pay hereabove market
This role pays more than
57%
of similar roles. Most pay
$196,350–$253,812
— the blue band above.
At the midpoint, this role pays about
$225k
versus about
$225k
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
131 open roles
on FindRole.
Listed pay typically runs
$190,400–$285,600
across 128 roles with salary data.
The Machine Learning Engineer, Radar joins the Radar ML team to build machine learning systems powering fraud and abuse protection products. You will own machine learning problems from conception to production by analyzing fraud patterns, exploring large-scale datasets, experimenting with model architectures, and designing evaluations. Your daily work involves building, training, and deploying models to detect fraud across a global network, including developing transformer architectures for payment data, building graph-powered intelligence, and leveraging LLMs to detect emerging attacks. You must possess strong foundations in machine learning, statistics, and experiment design alongside software engineering skills for writing maintainable code. The role addresses the technical challenge of identifying adversarial patterns in noisy environments while balancing model quality with latency, reliability, and operational complexity for global payment methods.
What does a Machine Learning Engineer earn in New York?
Median $231150 from 50 postings across 20 companies.
Build, train, evaluate, and deploy models to detect fraud and abuse across the global network.
Investigate emerging fraud patterns and develop rapid ML solutions to address them.
Design features, datasets, evaluation methods, and experiments for noisy and adversarial environments.
Improve model architectures and production systems to increase quality, coverage, and adaptability.
Monitor production behavior and iterate on models as data and attack patterns change.
Balance model quality with latency, reliability, and impact on legitimate users.
Develop reusable tools and AI-assisted workflows to improve ML system operations.
What we're looking for
2+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design.
Strong programming and software-engineering skills with experience writing tested, maintainable code for production ML applications.
Experience building or operating production ML systems including large-scale data pipelines, distributed training, low-latency inference, or production monitoring.
Ability to investigate ambiguous problems, reason from data, and communicate technical ideas clearly across disciplines.
Experience owning an ML problem across the full lifecycle from problem framing to monitoring (preferred).
Experience developing or productionizing modern deep learning, graph-learning, or LLM-based approaches (preferred).
Experience applying ML to fraud detection, integrity, trust and safety, or other adversarial domains (preferred).
Machine Learning
LLM
Graph Learning
Sequence Modeling
Deep Learning
Production ML
Real-time Inference
Model Evaluation
Experiment Design
Statistics
Monitoring