
Machine Learning Strategist
1 day ago
We are seeking a seasoned machine learning expert to spearhead the development and implementation of cutting-edge AI models. This role involves leading a team of ML engineers and collaborating closely with cybersecurity experts to create scalable, performant, and security-aware AI systems.
Key Responsibilities:
- Develop and fine-tune open-source large language models using frameworks like vLLM, HuggingFace, DeepSpeed, and PEFT techniques.
- Create 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 large language models.
- Design and execute 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 large language model architectures, cybersecurity datasets, and AI-based threat detection.
Required Skills and Qualifications:
- 5+ years of experience in machine learning, with at least 2 years focused on large language model 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, machine learning 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.
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