Telnyx - Machine Learning Engineer, Fraud & Abuse Detection

Dublin, Ireland; São Paulo, Brazil; Buenos Aires, Argentina2d ago

In Office Mid LATAM Artificial Intelligence Software Machine Learning Engineer Fraud Analyst Learning & Development Reporting FastAPI Python PostgreSQL Redis Kafka ClickHouse Docker Kubernetes Prometheus Ray

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 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. • 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).

Responsibilities

• 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.