AI & Security Intelligence Engineer

You will explore how language models and machine learning can support security analysis. The work is designed to make evidence, uncertainty, and the reasoning behind a recommendation visible.

• Develop and evaluate AI models for threat detection and anomalous behavior identification.
• Build scalable data pipelines to ingest and process heterogeneous security telemetry across the enterprise stack.
• Design correlation methods to support evidence-based risk prioritization.
• Collaborate with infrastructure engineers on analysis that supports action and validation.

Develop and evaluate threat intelligence sources. Study emerging attack vectors and the quality of exposure data used for analysis, including gaps and conflicting evidence.

Implement guardrails for AI-supported analysis, with attention to transparency, bias mitigation, and data privacy. Evaluate recommendations against evidence. AI confidence is never treated as proof.

• Degree in Computer Science, AI, or Cybersecurity, or equivalent practical experience.
• Strong proficiency in Python, PyTorch, or TensorFlow.
• Experience with LLM fine-tuning and retrieval-augmented generation (RAG) within security contexts.
• Deep understanding of the MITRE ATT&CK framework and vulnerability management.
• A mindset focused on engineering safety and mission-critical reliability.

This is not a purely theoretical research position or a traditional SOC analyst role. You will write and evaluate product code, with technical ownership and a bias toward action.

The work should make security analysis easier to inspect: what evidence supports a recommendation, what remains uncertain, and how to verify whether the security condition changed.

Help shape the technical direction of the company.

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