Lead the fine-tuning and domain adaptation of open-source LLMs (e.g., LLaMA 3) using frameworks like vLLM, HuggingFace, DeepSpeed, and PEFT techniques.
Develop data pipelines to ingest, clean, and structure cybersecurity data, including threat intelligence reports, CVEs, exploits, malware analysis, and configuration files.
Collaborate with cybersecurity analysts to build taxonomy and structured knowledge representations to embed into LLMs.
Drive the design and execution of evaluation frameworks specific to cybersecurity tasks (e.g., classification, summarization, anomaly detection).
Own the lifecycle of model development including training, inference optimization, testing, and deployment.
Provide technical leadership and mentorship to a team of ML engineers and researchers.
Stay current with advances in LLM architectures, cybersecurity datasets, and AI-based threat detection.
Advocate for ethical AI use and model robustness, especially given the sensitive nature of cybersecurity data
Requirements
Required Skills:
5+ years of experience in machine learning, with at least 2 years focused on LLM training or fine-tuning.
Strong experience with vLLM, HuggingFace Transformers, LoRA/QLoRA, and distributed training techniques.
Proven experience working with cybersecurity data—ideally including MITRE ATT&CK, CVE/NVD databases, YARA rules, Snort/Suricata rules, STIX/TAXII, or malware datasets.
Proficiency in Python, ML libraries (PyTorch, Transformers), and MLOps practices.
Familiarity with prompt engineering, RAG (Retrieval-Augmented Generation), and vector stores like FAISS or Weaviate.
Demonstrated ability to lead projects and collaborate across interdisciplinary teams.
Excellent problem-solving skills and strong written & verbal communication.
Nice to Have
Experience deploying models via vLLM in production environments with FastAPI or similar APIs.
Knowledge of cloud-based ML training (AWS/GCP/Azure) and GPU infrastructure.
Background in reverse engineering, malware analysis, red teaming, or threat hunting.
Publications, open-source contributions, or technical blogs in the intersection of AI and cybersecurity.
