Machine Learning Engineer, Fraud & Abuse Detection

The Role

As a Machine Learning Engineer on the Messaging team, you will own the systems that decide — in real time — which of the many millions of outbound messages a day reach real people and which get stopped. You'll build and operate fraud and abuse detection for our messaging platform: phishing and smishing, brand impersonation, artificially inflated traffic (AIT), and social-engineering scams targeting real users.

You'll join at an inflection point. We're rebuilding our detection stack on a modern decision-engine architecture — inline hot-path decisioning, a complete decision audit ledger, and entity-level risk state — with active programs in social-engineering, phishing, and brand-impersonation detection.

What You Will Do

• Design, train, evaluate, and operate text classifiers end-to-end: data curation, labeling, offline evaluation, deployment, and production drift monitoring.
• Run shadow-mode evaluations against labeled fraud corpora and human-reviewed legitimate controls before any model earns enforcement authority; report precision, recall, and calibration per abuse archetype.
• Build detection that holds up against adversarial content: obfuscated and confusable brand text, URL and domain evasion, and cross-account template fingerprinting.
• Serve detection models inline on the message send path under strict latency budgets, with deliberate fail-open/fail-closed behavior per dependency.
• Power human-in-the-loop fraud review: reviewer tooling, label quality, and turning fraud dispositions into replay and training signals.
• Collaborate with fraud operations, trust & safety, and cross-functional engineering teams.
• Participate in code reviews and help raise code quality and standards.

What You Bring

• 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 trust & safety.
• Rigorous evaluation methodology: labeled corpora, holdout sets, precision/recall tradeoffs, and calibration.
• Experience using LLMs as classifiers or judges within a larger decision pipeline.
• Experience deploying and maintaining ML models in production.
• Excellent communication skills and the ability to work effectively with cross-functional teams.

Nice to Have

• Familiarity with A2P messaging abuse: artificially inflated traffic, smishing, brand impersonation, and 10DLC / toll-free verification regimes.
• Adversarial ML experience: evasion-resilient detection, obfuscation handling, or attack-surface hardening.
• High-scale data experience with ClickHouse, PostgreSQL, Redis, and streaming systems such as Kafka.
• Experience with a cloud-native stack (e.g. Docker, Kubernetes, Prometheus).
• Experience with distributed batch scoring (e.g. Ray).