ML Researcher (Cybersecurity)

What You'll Do

• Research and prototype Agentic AI models and autonomous reasoning frameworks for cybersecurity use cases (threat detection, incident triage, response recommendations, attack simulation, etc.).
• Develop and evaluate machine learning algorithms for behavioral detection, anomaly analysis, and alert correlation.
• Collaborate with cybersecurity researchers, data engineers, and product teams to transform research results into production-grade AI features.
• Analyze large-scale telemetry and security datasets to derive actionable insights and support model improvement.
• Contribute to architecture and data pipeline design for scalable ML experiments and model deployment.
• Present findings, write technical papers or internal reports, and clearly communicate research impact to both technical and non-technical audiences.
• Stay ahead of the curve in AI/ML, autonomous agents, cybersecurity analytics, and LLM-based reasoning.
• Act independently on research directions and manage priorities with minimal supervision.

What You Bring

• Master’s or PhD in Computer Science, Machine Learning, or related field.
• 3+ years of experience in AI/ML research or applied ML, ideally in the context of cybersecurity, threat detection, or automation.
• Solid understanding of LLMs, reinforcement learning, retrieval-augmented generation (RAG), and agentic frameworks.
• Strong proficiency in Python and modern ML frameworks (PyTorch, HuggingFace, LangChain, etc.).
• Experience with data engineering and working with large, noisy datasets.
• Understanding of cybersecurity concepts such as malware behavior, intrusion detection, or incident response workflows is a plus.
• Excellent communication skills—able to summarize complex topics clearly and effectively.
• Self-driven, curious, and capable of managing end-to-end research projects independently.