Machine Learning Engineer, Fraud & Abuse Detection

About Telnyx

Telnyx is an industry leader that's not just imagining the future of global connectivity—we're building it. From architecting and amplifying the reach of a private, global, multi-cloud IP network, to bringing hyperlocal edge technology right to your fingertips through intuitive APIs, we're shaping a new era of seamless interconnection between people, devices, and applications.

We're driven by a desire to transform and modernize what's antiquated, automate the manual, and solve real-world problems through innovative connectivity solutions. As a testament to our success, we're proud to stand as a financially stable and profitable company. Our robust profitability allows us not only to invest in pioneering technologies but also to foster an environment of continuous learning and growth for our team.

Our collective vision is a world where borderless connectivity fuels limitless innovation. By joining us, you can be part of laying the foundations for this interconnected future. We're currently seeking passionate individuals who are excited about the opportunity to contribute to an industry-shaping company while growing their own skills and careers.

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).