AI Research Engineer

Responsibilities

• Build, train, and evaluate machine learning models that detect security threats and unusual system behavior
• Develop and maintain production AI features: prompt orchestration, retrieval-augmented generation (RAG), model serving, and observability
• Work with raw security data — logs, network traffic, event streams — to build reliable training datasets
• Build and maintain automated pipelines for model performance reporting and operational workflows
• Design and maintain data ingestion and transformation services used by downstream AI systems
• Monitor models in production, identify performance issues, and ship fixes
• Test models for accuracy, bias, and reliability before they reach production
• Work closely with security analysts to understand detection requirements and translate them into model improvements
• Write clean, documented code that other engineers can read and use as a basis for implementation
• Contribute to engineering standards for how the team develops and deploys models
• Designing distributed training environments, optimizing computational efficiency, and managing GPU clusters.
• Fine-tuning & Evaluation - Working with large language models (LLMs) and deep learning models using techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).
  • Model Safety & Alignment -  Testing for vulnerabilities, mitigating biases, and ensuring models behave safely and predictably.