Machine Learning Engineer (Cybersecurity)

TL;DR

Machine Learning Engineer (Cybersecurity): Developing and deploying ML models to transform the Security Operations Center (SOC) from reactive to proactive with an accent on anomaly detection, threat identification, and SOC workflow automation. Focus on building scalable data pipelines for security logs, implementing LLM-powered tools for alert triage, and reducing false positives in threat detection.

Location: Onsite in Paris, France

Company

Privacy-focused company providing secure email, VPN, and other tools to over 100 million users worldwide.

What you will do

• Design and deploy ML models for security detection and anomaly identification to enhance SOC triage and response.
• Build and maintain data pipelines to process security logs, network traffic, and endpoint events into ML-ready datasets.
• Develop LLM-powered tools to automate repetitive SOC tasks such as alert triage, incident summarization, and report generation.
• Prototype and test new techniques in adversarial ML, graph-based threat correlation, and unsupervised anomaly detection.
• Implement monitoring and retraining mechanisms for production models to ensure reliability and scalability.

Requirements

• Proven experience in machine learning engineering or data science, ideally in a cybersecurity context.
• Proficiency in Python and strong knowledge of ML frameworks.
• Experience with data manipulation tools such as Pandas and NumPy.
• Familiarity with security data sources including SIEM logs, EDR telemetry, and network flow.
• Experience with data pipelines and storage technologies (e.g., Airflow, Kafka, Redis, Elasticsearch, Clickhouse).
• Must be based in Paris (Onsite)

Nice to have

• Prior experience in threat detection, SOC operations, or security automation.
• Knowledge of adversarial ML, graph analytics, or behavioral modeling in security contexts.
• Exposure to LLMs and AI engineering, including RAG, prompt engineering, and agent design.

Culture & Benefits

• Ownership via stock options provided from day one.
• Comprehensive health coverage and solid retirement options.
• Generous leave and wellness support.
• Daily provided lunch and snacks in the office.
• Coverage for public transport, bike allowances, or parking.
• Flexible working hours focused on outcomes rather than fixed schedules.