Machine Learning Engineer II (Behavioral Security)

Location: Remote - UK

Company

hirify.global develops behavioral AI and cybersecurity products that protect organizations from evolving email security and account takeover threats.

What you will do

• Develop machine learning algorithms and models for behavioral modeling and cybersecurity attack detection.
• Perform exploratory data analysis, feature engineering, model development, and evaluation.
• Work with cross-functional, infrastructure, and product engineering teams to translate requirements into ML solutions and productionize models.
• Monitor and improve production models through feature engineering, rules, and ML modeling.
• Build and maintain data and metric generation pipelines to assess system effectiveness.
• Participate in code reviews and contribute to machine learning best practices and operational excellence.

Requirements

• 3+ years of commercial experience as a Machine Learning Engineer or in a similar role.
• Knowledge of machine learning algorithms, statistics, and predictive modeling.
• Proficiency with Python, pandas, and scikit-learn.
• Awareness of MLOps and productionization best practices for machine learning models.
• Familiarity with SQL or Spark for data and metric generation pipelines.
• Ability to communicate technical concepts clearly to non-technical audiences.

Nice to have

• Experience with LLMs, cybersecurity, behavioral modeling, or large-scale ML systems and data infrastructure.
• Experience with Airflow or similar ML pipeline orchestration tools.
• PhD or equivalent experience in machine learning research.
• Familiarity with cloud computing platforms such as AWS or Azure.
• Experience with PyTorch or TensorFlow.

Culture & Benefits

• Work on account takeover detection and customer protection against evolving fraud and cybersecurity threats.
• Collaborate across machine learning, data science, infrastructure, product, and cross-functional teams.
• Contribute to technical goals, team direction, roadmap decisions, and organization-wide ML practices.
• Use AI-assisted tools in recruiting to prepare interview questions; hiring decisions are made by people.