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Senior Machine Learning Engineer / Applied Scientist (Fraud & Risk)

torentify • United State
Remote
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Torentify seeks a Senior Machine Learning Engineer to design, deploy, and optimize fraud detection models at scale. This role spans applied research, statistical modeling, and production engineering, requiring ownership of the full ML lifecycle. Candidates must have hands-on production experience, expertise in Python/SQL/Spark, and a strong background in fraud/risk domains.

Key Highlights
Ownership of the full machine learning lifecycle from experimentation to production deployment
Focus on fraud detection and risk decisioning with real-time decision support at scale
Collaboration with cross-functional teams to drive measurable business outcomes
Key Responsibilities
Design, develop, and deploy machine learning models for fraud detection and risk decisioning
Manage the full machine learning lifecycle, including experimentation, validation, and production deployment
Work with large datasets in distributed computing environments (e.g., Apache Spark)
Develop and improve model evaluation frameworks and performance metrics
Collaborate with cross-functional stakeholders to support measurable business outcomes
Technical Skills Required
Python SQL Apache Spark
Benefits & Perks
$140,000–$175,000 annual base salary
Annual performance bonus
Equity or stock options
Unlimited paid time off (PTO)
Comprehensive benefits package
Nice to Have
PhD or master's degree in Computer Science, Statistics, Mathematics, or related quantitative field
Experience in fraud, risk, payments, or trust and safety domains
Experience building machine learning models from the ground up

Job Description

About the Company

The hiring company is a rapidly growing technology company focused on solving complex fraud and risk challenges at scale. Its machine learning team develops production models that support millions of real-time decisions and help protect customers from fraud.

About the Role

The company is seeking a Senior Machine Learning Engineer / Applied Scientist to develop and deploy machine learning models for fraud detection and risk decisioning. This role combines applied research, statistical modeling, and production engineering, with ownership across the full machine learning lifecycle, from experimentation and validation to deployment.

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