Machine Learning Engineering Manager (AI)

Location: Budapest, Hungary; hybrid

Company

hirify.global builds technology that helps online merchants approve good orders, protect revenue, and reduce e-commerce fraud.

What you will do

• Lead and grow an ML engineering team through mentoring, career development, feedback, conflict resolution, and technical guidance.
• Manage a portfolio of machine-learning experiments, prioritizing hypotheses, compute, headcount, and iterations based on evidence.
• Balance near-term model improvement commitments with longer-horizon research bets and communicate roadmap decisions to stakeholders.
• Set rigorous standards for experimentation by connecting offline evaluation results with online production behavior.
• Own delivery cadence by converging experimental workstreams into end-to-end evaluated release candidates and shipping reliable models.
• Partner with Risk, platform, and infrastructure engineering teams on model performance, feature systems, training pipelines, and experimentation tooling.

Requirements

• Approximately 5+ years of experience in machine learning, data science, or ML-adjacent software engineering.
• At least 3 years of people-management experience, including career development, conflict resolution, and building high-performing teams.
• Deep expertise in either engineering or applied statistics, with working competence in the other.
• Experience leading work under uncertainty, changing direction based on evidence, and communicating decisions clearly.
• Excellent written and verbal communication, with strong technical documentation skills.
• Autonomy, commitment to correctness, reproducibility, reliability, and high-quality engineering practices.

Culture & Benefits

• Collaborative, geographically distributed organization with a strong learning and improvement culture.
• Stock options and an annual performance bonus or commissions.
• Pension contributions matched up to 3%.
• Health insurance available from day one.
• Paid team social events, mental wellbeing resources, and a dedicated Learnerbly learning budget.