Applied AI Engineer

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mid

$150k–$250k

H1B: no

Cylake Inc Applied AI Engineer at Cylake Inc develops AI and machine learning solutions for cybersecurity platforms. The role includes building agentic AI systems, optimizing large language models, and collaborating with security researchers and engineers.

Responsibilities

Join a small team building the next generation of cybersecurity products from the ground up. Led by industry veterans with a proven track record of success - you will get to architect, build, and deliver hugely impactful products with this world-class team. You will have the opportunity to grow your career and skills along with the company from the very start.
Develop state-of-the-art agentic AI systems for next-generation SOC, SOAR, and SIEM platforms.
Build and optimize large language models (LLMs) for cybersecurity and security operations use cases through post-training techniques.
Develop domain-specific evaluation frameworks and benchmarks to continuously improve model quality, accuracy, and efficiency.
Design and implement machine learning and deep learning solutions for security threat detection, including malware, phishing, anomaly detection, and attack activity classification.
Develop AI workflows and model pipelines that leverage large-scale security datasets.
Partner with security researchers to improve data quality, model evaluation, and cybersecurity AI capabilities.
Collaborate with software engineers to integrate AI models into scalable, production-ready security products.
Research and apply advancements in LLM architectures, AI agents, model inference, and serving optimization.
Required Experiences
5+ years of experience applying machine learning, deep learning, or generative AI to cybersecurity or related domains, and/or a Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
Proven track record of applying machine learning, deep learning, or generative AI techniques and delivering high-quality models and solutions into production environments end-to-end.

Qualification

Experience with LLM architectureContributions to AI/ML competitions

Required

5+ years of experience applying machine learning, deep learning, or generative AI to cybersecurity or related domains, and/or a Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related field
Proven track record of delivering high-quality models and solutions into production
Deep understanding of modern AI agent architectures and implementation patterns
Experience with LLM supervised fine-tuning, reinforcement learning, preference optimization, and model distillation
Strong understanding of supervised and unsupervised learning, reinforcement learning algorithms, model training, and evaluation on large-scale datasets
Experience with domain-specific feature engineering, data labeling, and data quality optimization
Strong proficiency in Python programming and experience with deep learning frameworks such as PyTorch

Preferred

Experience with LLM architecture, model pre-training, inference optimization, and model serving infrastructure.
Experience with big data streaming pipelines, large-scale analytics, and distributed data processing.
Prior experience applying AI to cybersecurity domains such as threat detection, anomaly detection, fraud detection, malware classification, network traffic analysis, webpage classification, or security operations.
Contributions to AI/ML competitions, hackathons, open-source projects, technical blogs, arXiv, or top-tier research publications.