Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Build and iterate on real-time fraud/risk models to score transactions during checkout and support Risk decisioning with an accent on real-time decisioning use cases and building strong feedback loops. Focus on experimentation and feedback-loop initiatives to improve labels and model quality over time, while maintaining strong reliability, observability, and documentation.

Location: Remote

Salary: €55,000 - €70,000 a year

Company

hirify.global is building an open, safe and sustainable marketplace for gamers, supporting close to 20m+ active users.

What you will do

• Build and iterate on real-time fraud/risk models (e.g., gradient boosting and anomaly detection approaches) to score transactions during checkout and support Risk decisioning.
• Own the full ML lifecycle for fraud detection models: data exploration, feature engineering, training, evaluation, deployment, monitoring, and continuous improvement.
• Design robust evaluation strategies for rare-event, highly imbalanced data, including handling delayed/partial ground truth and defining metrics aligned with business constraints.
• Partner closely with Risk, backend, and Data/Platform teams to productionize models behind an API, integrate with the risk engine, and improve model-driven decision flows (pre-/post-authorization).
• Drive experimentation and feedback-loop initiatives to improve labels and model quality over time, while maintaining strong reliability, observability, and documentation.

Requirements

• 3+ years of experience as a Machine Learning Engineer (or in a similar applied ML role), ideally working with risk/fraud, anomaly detection, credit/default modeling, or other rare-event classification problems.
• Strong Python skills and hands-on experience building and iterating on supervised ML models (e.g., Gradient Boosting/LightGBM or similar), including feature engineering and model evaluation.
• Proven ability to design and run robust experimentation and evaluation under real-world constraints (imbalanced data, delayed/late-arriving labels, noisy or partial ground truth).
• Experience taking models to production and supporting the full model lifecycle (training, deployment, monitoring, and iteration) in collaboration with engineering teams.
• Solid knowledge of ML metrics and decisioning (precision/recall, thresholding, calibration, offline vs. online performance) and how they translate into business outcomes.
• Familiarity with modern MLOps tooling and practices (e.g., MLflow) and working with feature stores (Databricks Feature Store or alternatives).

Nice to have

• Experience with real-time / streaming feature pipelines or infrastructure (e.g., Kafka, Flink, Feast) and building low-latency model services/APIs for real-time scoring.

Culture & Benefits

• Opportunity to join our Employee Stock Options program.
• Opportunity to help scale a unique product.
• Various bonus systems: performance-based, referral, additional paid leave, personal learning budget.
• Paid volunteering opportunities.
• Work location of your choice: office, remote, opportunity to work and travel.
• Personal and professional growth at an exponential rate supported by well-defined feedback and promotion processes.