Machine Learning Engineer - Fraud Risk

Rain
New York
Workplace: HybridFull timeUSD 50,000 - 999,000 annuallyFunction: Data Science & Machine LearningExperience: 5+ yearsEducation: bachelorsSkills: ["Collaboration","Ability to work autonomously","Manage ambiguity","Experimentation","Cross-functional communication"]

Build and scale production ML systems for fraud detection, anomaly detection, and behavioral analysis. Own end-to-end ML pipelines from data ingestion and feature engineering through training, deployment, and continuous monitoring. Design low-latency real-time decision systems and maintain ML infrastructure with versioning, retraining, and safe releases. Drive monitoring/alerting and experiments for explainability, drift detection, and adversarial robustness, partnering with data scientists, compliance, and platform teams to meet SLAs.

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FursaFursa
Rain
Rain
7 months ago

Machine Learning Engineer - Fraud Risk

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 17 hours agoStatus: Live

Job Summary

Build and scale production ML systems for fraud detection, anomaly detection, and behavioral analysis. Own end-to-end ML pipelines from data ingestion and feature engineering through training, deployment, and continuous monitoring. Design low-latency real-time decision systems and maintain ML infrastructure with versioning, retraining, and safe releases. Drive monitoring/alerting and experiments for explainability, drift detection, and adversarial robustness, partnering with data scientists, compliance, and platform teams to meet SLAs.
Location: New York
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis.
  • •Develop and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, deployment, and continuous monitoring.
  • •Design and implement low-latency real-time decision systems integrating with transaction or behavioral data streams.
  • •Own ML infrastructure including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback).
  • •Build monitoring and alerting for model performance, latency, data quality, and drift, and lead experimentation for explainability, drift detection, and adversarial robustness.

Pay and Benefits

Salary: USD 50,000 - 999,000 annually
Equity and Bonus:Equity
Perks:Paid LeaveHealth InsuranceDentalVision401k

Key Requirements

  • •5+ years building ML systems in production, including 2+ years in fraud, risk, or anomaly detection domains.
  • •Advanced proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
  • •Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling.
  • •Proven track record designing and maintaining ML models at scale.
  • •Experience developing and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques.
Experience:5+ years
Education:Bachelor's in Computer Science, Engineering, Statistics, Applied Math, or related technical field
Skills:CollaborationAbility to work autonomouslyManage ambiguityExperimentationCross-functional communication
Tech Stack:PythonPyTorchTensorFlowScikit-learn

Company Brief

Rain
Rain builds stablecoin-powered card issuance and payments infrastructure for fintechs, platforms, and institutions. Its API and modular platform support on-ramps, wallets, cards, and off-ramps with compliance features like KYC and AML.
Industry: Fintech Infrastructure
Company Size: Medium (51 to 250 employees)
Growth: Growth Stage Startup
Headquarters: New York City, United States
Founded: 2021
WebsiteLinkedIn