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Popular ML/AI title, mid-level experience, metro location, and known employer create high candidate density.
Core ML skills transfer across industries, but security-detection and production-inference expertise increases domain specificity.
Specific LLM fine-tuning, production deployment, and inference-optimization skills required, but only 2+ years specified.
Design, train, fine-tune, and deploy lightweight, high-performance ML models for security detection use cases at scale.
Develop and optimize ML pipelines including fine-tuning of LLMs and transformer models focusing on latency, inference cost, throughput, and operational reliability.
Collaborate with security researchers and platform/product teams to turn ML techniques into robust production detection capabilities and maintain model monitoring and improvements.
Minimum 2+ years experience in Machine Learning Engineering or Applied AI.
Strong experience building and deploying ML systems in production, including fine-tuning transformer models or LLMs.
Proficient in Python and modern ML frameworks such as PyTorch or Hugging Face; knowledge of TensorFlow optional.
Experience optimizing models for inference efficiency and scale; experience deploying models in cloud or containerized environments.
Experienced in production-grade ML system development with a focus on security domain or related fields.
Skilled in optimizing ML models for low-latency, cost-efficiency, and high throughput in real-world applications.
Collaborative operator who can interface effectively between security research, product, and engineering teams to translate ML advances into practical detection solutions.