AI/ML Engineer - Cybersecurity

Role overview Build and improve applied AI systems that help security teams triage vulnerabilities and produce useful outcomes in real operating conditions. The role covers model quality and evaluation, data and feedback pipelines, production monitoring, and controlled releases, with attention to reliability, latency, and cost.

Responsibilities - Fine-tune and evaluate LLM workflows for vulnerability triage. - Build data pipelines for training, evaluation, and collection of feedback. - Work with security engineers to validate model outputs against real-world use cases. - Define and monitor measures of model quality, reliability, and production performance. - Release AI features through staged rollouts with appropriate controls.

Requirements - Experience building and shipping machine learning or AI systems, including in production. - Strong Python skills and a sound foundation in model evaluation. - Knowledge of NLP, LLMs, and current model development workflows. - Experience with prompt design, retrieval workflows, and LLM application patterns. - Working knowledge of cybersecurity concepts and use cases. - Ability to balance output quality, latency, and operating cost.

Nice to have - Experience with adversarial machine learning or secure AI systems. - Background in vulnerability management or cybersecurity operations. - Hands-on experience with vector databases and retrieval systems.