machine learning engineer in email security

Halon provides secure, flexible, scalable, and controllable solutions for dynamic business email operations. Its email security platform protects businesses from email-based threats and supports innovation through cloud-native security technologies.

• Design and implement machine learning models using LLMs, Transformers, and reputation systems to detect and mitigate phishing, malware, and business email compromise;
• Develop services for automated threat triage, content anonymization, and security intelligence retrieval from large datasets;
• Build and manage end-to-end machine learning pipelines for model training, evaluation, and deployment;
• Transform prototypes into production-ready data and machine learning applications meeting throughput and latency requirements;
• Work with neural network and tree-based models and manage them using tools such as MLflow;
• Drive the transition from manual processes to an automated MLOps environment with CI/CD pipelines, testing, alerting, and advanced logging;
• Create and maintain ETLs for large-scale data processing, reporting, model training, and decision-making while ensuring data integrity and confidentiality;
• Collaborate with data scientists, security engineers, and stakeholders to deploy and maintain threat detection models in production.

• Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field, or a Bachelor’s degree with equivalent senior-level industry experience;
• Experience contributing to multiple high-impact machine learning projects with proven results;
• Proficiency in Python and ability to write clean, structured, maintainable code for data analysis, modeling, and experimentation;
• Hands-on NLP experience training, fine-tuning, and productionizing transformer-based models for text classification and text embeddings;
• Proven experience with LLMs and generative AI;
• In-depth experience with one or more deep neural network frameworks, such as PyTorch, TensorFlow, or JAX;
• Experience monitoring and maintaining model performance in production, including model and data drift;
• Expertise in MLOps, including MLflow, Docker, and Kubernetes;
• Creative mindset and willingness to encourage critical thinking and novel approaches within the team;
• Nice to have: email security experience, spam and phishing detection, targeted threat protection, email traffic anomaly detection, Business Email Compromise modeling, Graph Neural Networks, large imbalanced datasets, Rust.

• Remote work across the European Union.