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Senior Engineering Manager, Applied Machine Learning

Extrahopnetworks
6 days ago
Full-time
Remote
Worldwide
Remote Engineering

At ExtraHop, we’re on a mission to protect and empower the connected enterprise. We reveal what is happening in the very infrastructure that sustains businesses, lives, and communities, and ensure the integrity of networks, data, systems, and processes. Organizations rely on ExtraHop to provide visibility into the cyber threats, vulnerabilities, and network performance issues that evade their existing security and IT tools. With this insight, organizations can investigate smarter, stop threats faster, and keep operations running.

Our mission is fueled by a profound social and moral responsibility to be the best at what we do, ensuring a secure world where everyone can thrive. If this sounds like a place you’d like to spend the next chapter of your career, we’d love to hear from you. 

Position Summary

ExtraHop is seeking a Senior Engineering Manager to lead the Applied Machine Learning team responsible for behavioral detections within the ExtraHop Network Detection and Response (NDR) platform.

This team develops machine learning systems that analyze large-scale network telemetry and surface meaningful behavioral signals for Security Operations Center (SOC) analysts. The work sits at the intersection of applied machine learning, cybersecurity, and high-volume time-series data.

This role owns the applied machine learning strategy for behavioral detection within the product. You will lead a team responsible for designing, evaluating, and operationalizing models that identify anomalous or suspicious patterns in complex network activity. The position combines technical leadership, scientific rigor, and product influence to ensure machine learning capabilities translate directly into actionable security insights.

Key Responsibilities

  • Lead and grow a multidisciplinary team of data scientists and software engineers building production machine learning models and supporting systems for behavioral detection.
  • Drive the research, development, evaluation, and operational monitoring of models that analyze large-scale network telemetry, including time-series and behavioral anomaly detection.
  • Establish high standards for experimental rigor across the team, including statistically sound experimentation, clear evaluation methodologies, and disciplined model validation.
  • Own the technical direction for production ML systems supporting behavioral detections, including experimentation frameworks, model lifecycle management, data pipelines, and monitoring.
  • Collaborate closely with Product Management and Security Research to translate machine learning capabilities into practical detection signals that improve SOC analy