ML Engineer — Fraud Detection

Best AI Tools Wiki; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Equity package

Remote work flexibility

Annual learning budget

Wellness stipend

Parental leave

Job summary

Stripe is seeking an ML Engineer to build and deploy real-time ML models for fraud detection and risk scoring. You will contribute to protecting millions of businesses from fraudulent transactions while keeping legitimate payments frictionless.

Ideal candidates have 4+ years of ML engineering experience, strong Python skills, and expertise in production ML frameworks. Experience with real-time inference at scale and imbalanced classification is valued.

Qualifications

• 4+ years of ML engineering experience.
• Experience with fraud detection or anomaly detection systems.
• Strong skills in Python and production ML frameworks.
• Experience with real-time inference at scale.
• Understanding of imbalanced classification problems.

Responsibilities

• Build and deploy real-time ML models that protect millions of businesses from fraud.
• Collaborate with cross-functional teams to integrate models into production systems.
• Monitor models and maintain performance in live fraud-detection environments.

Skills

ML engineering experience

Fraud detection

Python

Production ML frameworks

Real-time inference

Imbalanced classification

Job description

Stripe is seeking an ML Engineer to work on fraud detection and risk scoring systems. You will build and deploy real-time ML models that protect millions of businesses from fraudulent transactions while maintaining low friction for legitimate payments.

Requirements

• 4+ years of ML engineering experience
• Experience with fraud detection or anomaly detection systems
• Strong skills in Python and production ML frameworks
• Experience with real-time inference at scale
• Understanding of imbalanced classification problems

Nice to Have

• Experience in fintech or payments industry
• Background in graph neural networks
• Experience with online learning systems
• Knowledge of PCI compliance and financial regulations

Equity package

Remote work flexibility

Annual learning budget

Wellness stipend

Parental leave