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Salary: USD 150,000 - 250,000 / annual
Cylake is building next-generation cybersecurity products and is seeking an Applied AI Engineer to develop state-of-the-art AI capabilities for their platform. You will work at the intersection of artificial intelligence and cybersecurity, developing agentic AI systems, machine learning solutions, and training deep learning and LLM models that enable security teams to detect, investigate, and respond to threats at scale.
Key responsibilities include:
- Develop agentic AI systems for SOC, SOAR, and SIEM platforms
- Build and optimize large language models (LLMs) for cybersecurity use cases through post-training techniques like supervised fine-tuning, reinforcement learning, and preference optimization
- Create domain-specific evaluation frameworks and benchmarks to improve model quality and efficiency
- Design machine learning and deep learning solutions for threat detection (malware, phishing, anomaly detection, attack classification)
- Develop AI workflows and model pipelines leveraging large-scale security datasets
- Partner with security researchers to enhance data quality and cybersecurity AI capabilities
- Collaborate with software engineers to integrate AI models into production-ready security products
- Research and apply advancements in LLM architectures, AI agents, model inference, and serving optimization
Required qualifications:
- 5+ years applying machine learning, deep learning, or generative AI to cybersecurity or related domains, OR Ph.D. in Computer Science, AI, Machine Learning, or related field
- Proven track record delivering high-quality ML/DL/GenAI models and solutions into production end-to-end
- Deep understanding of modern AI agent architectures and implementation patterns
- Experience with LLM fine-tuning, reinforcement learning, preference optimization, and model distillation
- Strong foundation in supervised/unsupervised learning, reinforcement learning, and large-scale dataset evaluation
- Domain-specific feature engineering, data labeling, and data quality optimization experience
- Strong Python proficiency and deep learning framework experience (PyTorch preferred)
- Familiarity with databases, large-scale data processing systems, and production ML pipelines
- Excellent communication and technical documentation skills
Preferred experience includes LLM architecture and pre-training, inference optimization, big data streaming pipelines, prior cybersecurity AI applications, and contributions to AI/ML competitions or research publications.
