As an Experienced AI Researcher, you apply machine learning methods to real-world cybersecurity problems — preparing behavioral data, building and evaluating models, and guiding them toward production. Beyond that, you maintain and improve deployed models across their full lifecycle, from monitoring to continuous optimization. 

About the Role 

• Prepare and process behavioral data for training models that detect malicious activity. 

• Build and evaluate prototypes, iterating on architectures and features until production-quality, on dedicated in-house GPU servers — enough compute to compete with the biggest players in the industry. 

• Deploy models into production and integrate them with existing detection and incident-handling pipelines. 

• Monitor, maintain, and optimize deployed models across their full lifecycle — from drift detection to retraining and performance tuning. 

• When opportunities arise, explore novel architectures beyond applying existing models. 

Essential Requirements 

• Practical understanding of modern AI/ML methods — knowing which models fit which problems, when to use simpler approaches versus novel architectures, and how to optimize them for production. 

• Proficiency in Python and its AI/ML ecosystem. 

• Experience with AI/ML lifecycle tooling and infrastructure for production model serving and lifecycle management. 

• Ability to work independently and take full ownership of model development from start to finish. 

• Strong drive for continuous learning and staying current with state-of-the-art research. 

Desirable Requirements 

• University degree in Machine Learning, Data Science, Mathematics, or a related technical field. 

• Basic understanding of computer and network security. 

• Experience with Big Data technologies and cloud platforms (AWS). 

Technologies 

Python, Scikit-learn, PyTorch, TensorFlow, Polars, Hadoop, Spark, Hive, Impala, Iceberg, LanceDB, (My)SQL, Kafka, MLflow, Kubeflow, Argo, Git, JIRA, Confluence, Docker, Kubernetes, Jenkins, AWS.