18 days ago
Taipei, TaiwanMid Level
Responsibilities
- Design and deploy scalable ML systems with real-time inference, model monitoring, and automated retraining pipelines.
- Architect agentic AI systems for autonomous threat detection, adaptive policy enforcement, and self-healing security responses.
- Build tools for model development, debugging, and explainability.
- Optimize ML workflows for distributed environments and large-scale data processing.
- Collaborate with security analysts, backend and frontend engineers, and customer support.
- Advance ML techniques for anti-abuse and account security, including adversarial robustness and graph-based anomaly detection.
Requirements
- Degree in Computer Science or a related quantitative field.
- At least 2 years of hands-on experience building and running ML systems.
- Production-level expertise shipping models that operate at real-world scale and complexity.
- Hands-on experience with LLMs in agentic environments.
- Proficiency in Python and strong backend and server fundamentals.
- Deep understanding of probability, hypothesis testing, and experimental design.
- Preferred experience with adversarial ML, ML red-teaming, distributed systems, federated learning, differential privacy, on-device inference, graph-based anomaly detection, knowledge graphs, or security and threat intelligence.
- Preferred technical leadership experience owning projects, mentoring peers, and driving technical direction.
Benefits
- Health coverage, retirement options, generous leave, and wellness support.
- Stock options and company ownership opportunity.
- Hardware and software needed to perform the work.
- Learning and development support with meaningful ownership from day one.
- In-person collaboration at offices including Geneva, Zürich, and Barcelona, with most work conducted face-to-face.
- Flexible working hours and schedule.
- Lunch and snacks provided in offices.
- Public transport, bike allowance, or parking coverage.
- Global, collaborative, privacy-focused work protecting millions of users.
Tech Stack
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