Lead ML Architect at high-growth cybersecurity AI startup

You will architect a sophisticated Context Engine that resolves the volume crisis in cybersecurity. By bridging the gap between rigid infrastructure data and semantic LLM reasoning, you will build a system that transforms thousands of raw alerts into actionable focus areas using entity resolution, multi-membership clustering, and deterministic logic guardrails.

High-growth cybersecurity AI startup

• Design a feature engineering layer that translates hard infrastructure signals like IPs and CNAMEs into inductive biases for LLM reasoning.
• Implement a taxonomy-driven knowledge injection pipeline using deterministic lookup systems to ensure high-accuracy domain expertise.
• Create a logic layer for entity resolution to resolve canonical identities across sparse data points and define asset boundaries.

• Proven experience building complex data pipelines where LLMs are integrated as components rather than standalone solutions.
• Strong background in ML engineering, specifically categorical data normalization, feature engineering, and agentic workflow orchestration.
• Solid understanding of security literacy, including vulnerability patterns, infrastructure networking concepts, and deterministic deduplication methods.

• Lead the development of a core engine that moves beyond simple LLM wrappers into complex data modeling and explainable AI.
• Join a well-funded startup backed by top-tier VCs at an early stage where you define the technical authority and roadmap.
• Solve high-stakes engineering challenges involving sparse data points, canonical identity resolution, and massive-scale vulnerability correlation.