Applied AI Engineer
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What you'll do
- 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.
What they're looking for
- 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.
- Deep understanding of modern AI agent architectures, agent frameworks, and implementation patterns.
- Experience with LLM supervised fine-tuning (SFT), 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.
- Familiarity with databases, large-scale data processing systems, and production ML pipelines.
- Strong communication and technical documentation skills.
Nice to have
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Your Impact
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.
Role Overview
You will build state-of-the-art AI capabilities for Cylake's next-generation cybersecurity platform. Working at the intersection of artificial intelligence and cybersecurity, you will develop agentic AI systems, build machine learning solutions, and train deep learning and LLM models that enable security teams to detect, investigate, and respond to threats at scale.
You will collaborate closely with security researchers and software engineers to develop, productize, and deploy AI-driven security solutions across SOC, SOAR, and SIEM workflows.
Responsibilities
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.
Deep understanding of modern AI agent architectures, agent frameworks, and implementation patterns.
Experience with LLM supervised fine-tuning (SFT), 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.
Familiarity with databases, large-scale data processing systems, and production ML pipelines.
Strong communication and technical documentation skills.
Preferred Experience
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.
We offer competitive compensation and a comprehensive benefits package designed to support our employees’ health, well-being, and long-term success. The expected salary range for this position is $150,000 – $250,000 per year. Within this range, individual pay is determined based on job-related factors including skills, experience, qualifications, and internal equity. Most candidates can expect an offer within the range listed above. Your recruiter will provide additional details on compensation and benefits to qualified candidates during the hiring process.
We’re committed to building a diverse, inclusive workplace where everyone can do their best work. We are proud to be an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. If you require a reasonable accommodation during the application or interview process, please let us know — we’re happy to support you.
Company
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