Applied AI Engineer

What you'll do

• Develop agentic AI systems and optimize LLMs for SOC, SOAR, and SIEM use cases.
• Build evaluation frameworks, model pipelines, and ML/deep-learning solutions for detecting and classifying security threats.
• Integrate AI models into production security products and collaborate with security researchers and software engineers.

What they ask for

• 5+ years applying machine learning, deep learning, or generative AI to cybersecurity or related domains, and/or a Ph.D. in a related technical field.
• Track record delivering AI/ML solutions into production end-to-end; understanding of AI agent architectures and implementation patterns.
• Experience with LLM supervised fine-tuning (SFT), reinforcement learning, preference optimization, and model distillation.
• Strong Python proficiency and experience with deep learning frameworks such as PyTorch.

Nice to have

• Experience with LLM pre-training, inference optimization, and model serving infrastructure.
• Experience with big data streaming and distributed processing, cybersecurity AI, or AI/ML contributions to competitions, open source, blogs, arXiv, or research publications.

Pay

$150,000 – $250,000 per year (base salary)

Tools

Python, PyTorch, LLMs, SOC, SOAR, SIEM

AI focus

Develop agentic AI, train and post-train LLMs, create model evaluation frameworks, build ML and deep-learning threat-detection solutions, and optimize inference and serving.