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Cylake

Applied AI Engineer

Sunnyvale$150k–$250kfulltimemidAdded yesterday

About this role

Build cutting-edge AI systems for next-generation cybersecurity platforms at an early-stage company led by industry veterans. You'll develop agentic AI, optimize LLMs, and create machine learning solutions that help security teams detect and respond to threats at scale.

What you'll do

  • Develop agentic AI systems for SOC, SOAR, and SIEM platforms
  • Build and optimize LLMs for cybersecurity use cases through post-training techniques
  • Create domain-specific evaluation frameworks and benchmarks for model quality
  • Design machine learning solutions for threat detection, malware, and phishing classification
  • Develop AI workflows and pipelines leveraging large-scale security datasets
  • Collaborate with security researchers and engineers to productize and deploy AI solutions

What they're looking for

  • Machine learning and deep learning
  • Generative AI and LLM fine-tuning (SFT, RLHF, preference optimization)
  • AI agent architectures and frameworks
  • Python and PyTorch
  • Supervised and unsupervised learning
  • Cybersecurity domain knowledge
  • Production ML pipelines and model deployment
  • Feature engineering and data quality optimization

Benefits

  • Competitive compensation ($150,000–$250,000 annually)
  • Comprehensive benefits package
  • Early-stage company with growth opportunities
  • Work with industry veterans and world-class team
  • Career development and skill growth
  • Equal opportunity employer with inclusive workplace
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Cylake

Cylake builds an AI-native cybersecurity platform that combines endpoint security agents, observability infrastructure, and petabyte-scale data lakehouse capabilities to detect and prevent threats across systems. The company is hiring for platform engineers, data architects, frontend engineers, and systems engineers to build core infrastructure, analytics pipelines, security agent technology, and user experiences for threat investigation.

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Likely interview questions

  • Walk us through a machine learning project where you took a model from development to production in a security context.
  • How have you applied LLM fine-tuning or post-training techniques, and what challenges did you encounter?
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