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Adaptive Security

Founding Machine Learning Engineer

NYCfulltimemidAdded 1 month ago

About this role

Founding ML Engineer at an AI cybersecurity startup backed by NVIDIA and OpenAI. Build production ML systems from scratch to detect and classify AI-powered threats like deepfakes and voice scams, establish infrastructure and strategy, and scale a new ML function.

What you'll do

  • Define ML strategy and architecture across the company's cybersecurity products
  • Design and build end-to-end production ML systems including data pipelines, training, evaluation, and inference serving
  • Establish evaluation methodology and frameworks to measure model quality and prevent regressions
  • Develop data strategy including labeling, feedback loops, and continuous model improvement
  • Integrate ML systems into products and write production-quality code alongside engineers
  • Build and lead the ML team as the function grows

What they're looking for

  • Production ML systems design and implementation
  • Cloud ML infrastructure (AWS SageMaker, Bedrock, Modal, or similar)
  • Python, Java, or TypeScript programming
  • ML frameworks (PyTorch, TensorFlow, Spark)
  • Data pipeline and infrastructure development
  • Adversarial ML and security applications
  • Team leadership and mentoring
  • Full-stack system design

Benefits

  • Competitive cash compensation and meaningful equity
  • Fully covered medical plans
  • 401k through Vestwell
  • Unlimited PTO with guaranteed winter break
  • Free daily lunch from local restaurants
  • Meal and transportation expenses covered for late nights
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Adaptive Security

Adaptive Security builds an AI-era security platform that detects and classifies AI-powered threats like deepfakes and voice scams for 1,200+ customers. The company is hiring full-stack engineers and ML specialists to develop core product systems, detection pipelines, and production machine learning infrastructure.

View all jobs at Adaptive Security

Likely interview questions

  • Walk us through a time you built an ML system from scratch at an early-stage company or as the first/senior ML hire. What were the biggest infrastructure and organizational challenges?
  • How would you approach the data labeling and feedback loop strategy for detecting novel, adversarial attack vectors where labeled data is scarce?
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