# Machine Learning Engineer, Fraud & Abuse Detection

**Company**: Telnyx
**Location**: Dublin
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/telnyx54/jobs/8044370003?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_a98b1026-d19

## Description

## 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.

## 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).

## Skills

### Required
- async Python webservice frameworks
- text classification systems
- machine learning
- software development
- LLMs

### Nice to have
- A2P messaging abuse
- adversarial ML
- ClickHouse
- PostgreSQL
- Redis
- Kafka
- cloud-native stack
- distributed batch scoring

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/telnyx54/jobs/8044370003?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)