
Leading AI Innovation in Cybersecurity
1 day ago
This role involves overseeing the development and implementation of advanced machine learning models in a cybersecurity context.
The ideal candidate will have extensive experience with large language models (LLMs) and a strong background in both machine learning and cybersecurity.
Responsibilities include leading the fine-tuning of LLMs, developing data pipelines to ingest and structure cybersecurity data, and collaborating with analysts to build taxonomy and structured knowledge representations.
Key Responsibilities:
- Lead the adaptation of open-source LLMs using frameworks like vLLM, HuggingFace, and DeepSpeed.
- Develop data pipelines to process and organize cybersecurity data, including threat intelligence reports and CVEs.
- Collaborate with cybersecurity experts to develop taxonomy and structured knowledge representations.
- Design and execute evaluation frameworks for cybersecurity tasks, such as classification and anomaly detection.
Requirements:
- At least 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, including MITRE ATT&CK, CVE/NVD databases, YARA rules, Snort/Suricata rules, STIX/TAXII, or malware datasets.
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.
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