Machine Learning Researcher focusing on innovative AI and intelligent automation for cybersecurity. Driving research in Agentic AI and collaborating with cross-functional teams for production-grade features.
Responsibilities
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.
Requirements
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.
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