Tech – Data Scientist: LLM-Driven Red Team & Payload Automation

Big Data & Data Pipeline Engineering: Gain practical experience processing, sanitizing, and structuring massive unstructured cybersecurity datasets (8TB+) at scale for AI training pipelines. LLM Fine-Tuning & Quantization: Master state-of-the-art parameter-efficient fine-tuning (PEFT, QLoRA) and model optimization techniques across leading open-weight LLM architectures. AI Security Benchmarking: Build rigorous evaluation frameworks to systematically benchmark fine-tuning methodologies, payload accuracy, and execution validity. Automated Red Team Mechanics: Deepen your technical knowledge of software vulnerability mechanics, exploit structures, and automated payload execution within safe testing environments. Applied AI/ML Systems Engineering: Learn to bridge the gap between foundation models and practical security operations by deploying autonomous agentic pipelines.

- Strong proficiency in Python and familiarity with data processing frameworks (e.g., PySpark, Polars, or DuckDB) for handling large datasets. - Hands-on experience with modern machine learning frameworks (e.g., PyTorch, Hugging Face Transformers, or Unsloth) and fine-tuning techniques (e.g., LoRA, QLoRA). - Basic understanding of software security concepts, vulnerability mechanics, and basic exploit structures. - Comfortable working in Linux environments and writing shell scripts for task automation. - Prior experience with model evaluation, cybersecurity operations, or handling massive datasets (>1TB) will be an advantage.