(Security) Machine Learning Engineer

Requirements

• Proven experience in machine learning engineering or data science, ideally in a cybersecurity or operations context.
• Proficiency in Python, with strong knowledge of ML frameworks.
• Experience with data manipulation and analysis using Pandas, NumPy or similar tools.
• Familiarity with security data sources (e.g., SIEM logs, EDR telemetry, network flow, authentication logs).
• Solid understanding of ML lifecycle: data preparation, model training, evaluation, deployment, and monitoring.
• Experience with data pipelines and storage technologies (e.g., Airflow, Kafka, Redis, Elasticsearch, Clickhouse, etc.).
• Ability to work independently and collaborate effectively with both ML and security specialists.

Nice to Haves

• Prior experience in threat detection, SOC operations, or security automation.
• Knowledge of adversarial ML, graph analytics, or behavioral modeling in security contexts.
• Experience integrating ML models into SIEM pipelines or automated detection frameworks.
• Exposure to LLMs and AI engineering (e.g., prompt engineering, RAG, agent design), and awareness of LLM-specific risks like prompt injection and data leakage.

What You'll Be Doing

• Design, develop, and deploy machine learning models to enhance security detection, anomaly identification, and incident response.
• Integrate ML outputs into the SOC workflow to enable smarter and faster triage.
• Continuously evaluate and tune models to reduce false positives and improve detection precision.
• Ensure model outputs are interpretable and actionable for SOC analysts.

Perks and Benefits

• Work that Matters: millions of people trust Proton with their privacy.
• Technology: you'll get the right hardware and software you need.
• Learning & Development: we invest in your growth.
• Employee Benefits: strong health coverage, retirement options, generous leave, and wellness support.
• Stock Options: opportunity to be owners of the company.
• In-Person Collaboration: offices across multiple locations for face-to-face collaboration.
• Food: Lunch and snacks provided daily in offices.
• Transport: Public transport, bike allowances, or parking covered.
• Flexible Working: Own your schedule.