(Security) Machine Learning Engineer

Join Proton and build a better internet where privacy is the default

At Proton, we believe that privacy is a fundamental human right and the cornerstone of democracy. Since our inception in 2014, founded by a team of scientists from CERN, we have dedicated ourselves to providing free and open-source technology to millions worldwide, ensuring access to privacy, security, and freedom online.

Our journey began with Proton Mail, the largest secure email service globally, and has since expanded to include Proton VPN, Proton Calendar, Proton Drive, and Proton Pass. These tools empower individuals and organizations to take control of their personal data, break away from Big Tech’s invasive practices, and defeat censorship. Our work impacts hundreds of millions of lives, from activists on the front lines defending freedom to leaders in governments protecting sensitive information. In some cases, Proton’s services have even been instrumental in saving lives by enabling secure and private communications in high-risk situations.

Proton is a profitable company that does not rely upon VC funding, supporting over 100 million user accounts with a growing team of over 500 people from over 50 different countries, from the world's top companies and universities. We value intelligence, learning potential, and ambition in our hiring process. Adaptability is key as we navigate uncharted territories and redefine how business is conducted online.

Hiring at Proton is highly selective, with less than 1% of candidates hired. We believe smaller teams of exceptional talent will always prevail over larger teams with lower talent density. You will have the opportunity work with many of the world's top minds in their fields, ranging from former international math and science olympiad winners to chess champions.

We have a global mindset and big ambitions but remain a start-up at heart. We value empowerment and flexibility and keep our structure flat to keep moving fast and avoid unnecessary politics. Tired of blending into the crowd? Join us and do work you can truly be proud of. Check our open-source projects here!

The Team

The Security Machine Learning Engineer will play a key role in transforming our Security Operations Center (SOC) from reactive to proactive by integrating advanced machine learning and data-driven approaches into our detection and response workflows.

This role bridges traditional cybersecurity operations and modern ML-driven analytics, enabling our team to automatically identify emerging threats, anomalous behaviour, and new attack patterns at scale. As a secondary focus, the role could also leverage LLMs and AI engineering to automate analyst workflows and reduce operational toil.

The engineer will sit directly within the security team, ensuring that the solutions built are operationally relevant, and aligned with our security priorities, while also working closely with the internal Machine Learning team (MSA) to leverage their expertise and best practices.

What you will do:

• ML-Driven Detection & Automation

  • 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.

  Data Engineering for Security

  • Build and maintain data pipelines to collect, process, and transform security-relevant data (e.g., logs, network traffic, endpoint events) into ML-ready datasets.
  • Collaborate with security engineering team to ensure scalable and secure data handling (eg. parsing, processing, storage).

  AI Engineering & LLM-Powered Automation

  • Explore and build LLM-powered tools to automate repetitive SOC tasks (e.g., alert triage, evidence gathering, incident summarisation, report generation).
  • Apply appropriate guardrails and evaluation to ensure outputs are accurate, auditable, and safe to act on in operational contexts.

  Research & Innovation

  • Stay current on advancements in security data science, adversarial ML, and automated threat detection.
  • Prototype and test new ML and AI techniques (e.g., unsupervised anomaly detection, graph-based threat correlation).
  • Contribute to improving detection content through statistical analysis and clustering.

  Operations & Maintenance

  • Deploy models into production securely and responsibly, ensuring reliability and scalability.
  • Implement monitoring, alerting, and retraining mechanisms for deployed ML models.
  • Document methodologies and performance metrics for auditability and knowledge sharing.

What we are looking for:

• Required

  • 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.

  Preferred

  • Prior experience in threat detection, SOC operation