Machine Learning Engineer, Fraud & Abuse Detection

Location: Dublin, Ireland; São Paulo, Brazil; Buenos Aires, Argentina

Company

hirify.global builds global connectivity infrastructure, including a private multi-cloud IP network, edge technology, and APIs for connecting people, devices, and applications.

What you will do

• Design, train, evaluate, deploy, and operate text classifiers for phishing, smishing, brand impersonation, artificially inflated traffic, and social-engineering scams.
• Curate data, manage labeling, run shadow-mode evaluations against labeled fraud corpora and human-reviewed legitimate controls, and report precision, recall, and calibration by abuse archetype.
• Build adversarially resilient detection for 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 or fail-closed behavior by dependency.
• Develop human-in-the-loop fraud review tooling, improve label quality, and turn fraud dispositions into replay and training signals.
• Collaborate with fraud operations, trust and safety, and cross-functional engineering teams; participate in code reviews and improve code quality and standards.

Requirements

• 3+ years of software development and/or machine learning experience.
• Strong proficiency in asynchronous Python web service frameworks such as FastAPI.
• Production experience building and operating text classification systems in fraud, spam, abuse, or trust and safety.
• Experience with rigorous evaluation methodology, including labeled corpora, holdout sets, precision/recall trade-offs, and calibration.
• Experience using LLMs as classifiers or judges within a larger decision pipeline.
• Experience deploying and maintaining machine learning models in production.
• Excellent communication skills and the ability to work effectively with cross-functional teams.

Nice to have

• Familiarity with A2P messaging abuse, including artificially inflated traffic, smishing, brand impersonation, and 10DLC or toll-free verification regimes.
• Adversarial machine learning experience, including 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 cloud-native technologies such as Docker, Kubernetes, and Prometheus.
• Experience with distributed batch scoring tools such as Ray.

Culture & Benefits

• Work in a financially stable and profitable connectivity company.
• Contribute to a private, global, multi-cloud connectivity network and messaging platform.
• Access an environment focused on continuous learning and professional growth.