Principal Software Engineer | Data Science

ExtraHop develops network detection and response products that help organizations identify and address cybersecurity threats. The Principal Software Engineer will serve as a technical anchor for its NDR platform, leading architecture and engineering initiatives involving AI/ML models and autonomous AI agents while influencing technical direction and mentoring engineers.

Responsibilities:

• Design, write, and maintain robust, high-performance software to deploy AI/ML models that analyze network traffic for anomalous or malicious behaviors
• Design and develop autonomous AI agent architectures and multi-agent workflows
• Collaborate with data scientists, threat researchers, and cloud teams to build high-performance services, pipelines, and tooling that accelerate ML experimentation and production workflows
• Drive engineering excellence across teams through code reviews, design reviews, and the establishment of standards, patterns, and best practices
• Lead cross-functional technical initiatives and represent engineering in conversations with senior leadership and external stakeholders
• Contribute to product strategy by bringing deep technical insight to roadmap discussions, build-vs-buy decisions, and competitive differentiation
• Lead by example. Provide technical mentorship to software engineers on the team, fostering a culture of continuous learning, optimization, and rigorous engineering discipline

Requirements:

• Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• 8+ years of professional software engineering experience, with at least 2 years in Principal, Lead, or Staff capacity shipping enterprise-grade software
• Expert-level programming skills in Python and Go, with an emphasis on writing production-quality, well-tested, and modular code
• Experience in machine learning, statistics, or a related quantitative discipline
• Experience building or operating production AI/ML systems, including model lifecycle management, data/training pipelines, and monitoring
• Expertise in applying machine learning or data science techniques to cybersecurity use cases such as anomaly detection, behavioral analysis, or threat classification
• Advanced understanding of threat detection methodologies, intrusion prevention systems, and incident response workflows
• A strong fundamental understanding of network telemetry and protocols
• Prior experience working in the cybersecurity domain
• Familiarity with threat actor techniques and common network attack vectors
• Familiarity with machine learning applications in cybersecurity
• Understanding of threat detection, intrusion prevention, and incident response strategies
• Deep experience with cloud services on AWS or Azure
• Deep experience with containerization and orchestration technologies including Docker and Kubernetes at production scale
• Strong written and verbal communication skills, including the ability to influence technical and non-technical audiences at the executive level