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Machine Learning Engineer
Machine learningOn-site — Geneva, Taipei
Published on 2026-08-17
These details were extracted automatically from the original listing. They may not be complete or fully up to date — it's worth checking the original job post.
About this role
Build and operate production machine-learning systems for detecting abuse, protecting accounts, and improving mail security across Proton’s services. The role also focuses on agentic AI for autonomous threat detection and response, with systems handling large-scale real-time and asynchronous decisions.
About the company
Proton builds end-to-end encrypted email, VPN, storage, password, and related privacy tools for individuals and businesses.
The team
MSA team (Mail Delivery, Spam, and Anti-Abuse), a multidisciplinary group working across security challenges in the Proton ecosystem.
Stack
PythonLLMs
What you'll do
- Design and deploy scalable ML systems, including real-time inference, monitoring, and automated retraining.
- Architect agentic AI systems for threat detection, policy enforcement, and security response.
- Build tools for model development, debugging, and explainability.
- Optimize ML workflows for distributed, large-scale data processing.
- Collaborate with security analysts and backend/frontend engineers.
- Explore adversarial robustness, graph-based anomaly detection, and privacy-preserving AI.
What we're looking for
- Degree in computer science or a related quantitative field, plus 2+ years building and operating ML systems.
- Experience shipping production ML models at real-world scale.
- Hands-on experience with LLMs in agentic environments.
- Proficiency in Python and a solid understanding of backend and server fundamentals.
- Strong grounding in probability, hypothesis testing, and experimental design.
Nice to have
- Adversarial ML or ML red-teaming experience.
- Distributed-systems experience.
- Contributions to open-source security or privacy tools.
- Knowledge of federated learning, differential privacy, or on-device inference.
- Experience with graph-based anomaly detection or knowledge graphs.
- Technical leadership, project ownership, or mentoring experience.
- Familiarity with abuse patterns, adversarial attacks, or threat intelligence.
Benefits
- Stock options.
- Health coverage, retirement options, generous leave, and wellness support.
- In-person collaboration at company offices; most work is conducted face-to-face.
- Office lunch and snacks.
- Public transport, bike, or parking support.
- Flexible working hours.