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Artemis

AI/ML Engineer

New York CityfulltimemidAdded 1 month ago

About this role

Artemis is seeking an AI/ML Engineer to help build an AI-native cybersecurity platform that detects and defends against AI-driven attacks. You'll design and deploy large-scale LLM-powered systems to process exabytes of security data, working across classification, detection, and data infrastructure with significant ownership of end-to-end initiatives.

What you'll do

  • Design and ship end-to-end ML-driven features using LLMs for detection, classification, and analytics
  • Build and scale LLM pipelines to process massive volumes of security telemetry reliably
  • Make architectural decisions on model selection, prompting vs fine-tuning, and system design for scalability and cost-efficiency
  • Contribute across the full stack including LLM services, backend APIs, data infrastructure, and applications
  • Monitor and debug model degradation in production, optimizing for performance and reliability
  • Collaborate with product, design, security, and platform teams to turn complex problems into solutions

What they're looking for

  • Machine learning model training and deployment
  • Large language models and generative AI optimization
  • Feature engineering and statistical modeling
  • Distributed systems and large-scale data processing
  • Production ML systems monitoring and debugging
  • Backend engineering and API design
  • Data infrastructure and analytics
  • Python or similar ML engineering languages
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Artemis

Artemis builds an AI-driven cybersecurity platform that detects and defends against modern threats using large-scale LLM systems and intelligent threat detection. The company is hiring product engineers, security researchers, and ML engineers to develop full-stack customer-facing features, AI-powered security detections, and scalable infrastructure for processing security data.

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Likely interview questions

  • Walk us through a time you trained and deployed an ML model from scratch without relying on existing infrastructure. What were the biggest challenges and how did you overcome them?
  • Describe your experience optimizing a generative AI system. How did you develop metrics and validation sets, and how did you work with domain experts to evaluate performance?
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