Principal Machine Learning Engineer (Prisma AIRS) - NetSec

Job Summary

The Team: Engineering - Our Engineering team is at the core of our products and connected directly to the mission of preventing cyberattacks. We are constantly innovating and challenging the way we, and the industry, think about cybersecurity. Our engineers dont shy away from building products to solve problems no one has pursued before.

Key Responsibilities

• Lead SLM tuning initiatives for low-latency, highly accurate edge-cloud security tasks.
• Drive advanced data classification strategies using ML, NLP, and deep learning.
• Extract complex threat patterns from massive structured and unstructured datasets.
• Partner with product, security, and cloud engineering teams as a technical leader.
• Integrate machine learning solutions seamlessly into scalable production systems.

Qualifications

Required Qualifications

• MS or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related field.
• 14+ years of software engineering industry experience, including 3+ years in ML, NLP, or AI systems.
• Deep programming expertise in Python and deep learning frameworks like PyTorch or TensorFlow.
• Proven experience working directly with LLMs/SLMs, advanced prompt engineering, and fine-tuning techniques.
• Strong background in building and scaling auto-classification and anomaly detection models on massive datasets.

Preferred Qualifications

• Demonstrated knowledge of cybersecurity concepts and AI safety vulnerabilities like prompt injection defense and AI red-teaming.
• Familiarity with DistilBERT and other open-source classification models.
• Experience with large, graph-based datasets and processing techniques.
• Expertise in distributed cloud systems (GCP or AWS) and deploying ML inference pipelines at scale.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.