Machine Learning Engineer, Fraud & Abuse Detection

📋 Description

• Design, train, evaluate, and operate text classifiers end-to-end: data curation, labeling, offline
• Run shadow-mode evaluations against labeled fraud corpora and human-reviewed legitimate controls
• Build detection that holds up against adversarial content: obfuscated and confusable brand text
• Serve detection models inline on the message send path under strict latency budgets, with
• Power human-in-the-loop fraud review: reviewer tooling, label quality, and turning fraud
• Collaborate with fraud operations, trust & safety, and cross-functional engineering teams.

🎯 Requirements

• 3+ years of software development and/or machine learning experience.
• Strong proficiency in async Python webservice frameworks (e.g. FastAPI).
• Production experience building and operating text classification systems in fraud, spam, abuse, or
• Rigorous evaluation methodology: labeled corpora, holdout sets, precision/recall tradeoffs, and
• Experience using LLMs as classifiers or judges within a larger decision pipeline.
• Experience deploying and maintaining ML models in production.