Principal Group Engineering Manager

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Software Engineering

Principal Group Engineering Manager for Microsoft Defender's real-time protection services, leading a global team to build and scale AI-driven detection capabilities, modernize the protection stack, and unify threat intelligence. The role focuses on operating always-on, low-latency cloud services powered by hundreds of ML models at planetary scale.

What you'd actually do

  1. Lead a globally distributed engineering organization across North America and Europe, spanning real-time systems, large-scale data platforms, machine learning, and threat intelligence.
  2. Modernizing the protection stack onto Kubernetes, modern .NET, and ARM-based compute
  3. Unifying fragmented threat intelligence into a single platform and API layer leveraged across Microsoft Security
  4. Scaling AI-driven detection capabilities
  5. Ensuring services remain always-on and continuously improve in efficiency at global scale (supporting over a billion users)

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field
  • 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

Nice to have

  • Master's Degree in Computer Science or related technical field AND 12+ years technical engineering experience
  • 15+ years technical engineering experience
  • 8+ years of people management experience, including experience leading multiple teams and managing other managers
  • 8+ years of experience designing, delivering, and operating large-scale distributed systems or cloud services in production
  • Proven track record operating always-on, low-latency, high-availability cloud services at global scale (high request-per-second, multi-region, including sovereign clouds)
  • Domain background in security — threat protection, threat intelligence, anti-malware, web/URL or network protection, EDR, or detection systems
  • Experience leading AI/ML-driven systems and large-scale data platforms (e.g., telemetry, model training and serving, graph-based intelligence)

What the JD emphasized

  • critical and complex technical agenda
  • cannot be wrong, and it cannot be slow
  • always-on
  • low-latency
  • global scale

Other signals

  • scaling AI-driven detection capabilities
  • hundreds of ML models
  • planetary scale signal
  • real-time protection
  • low-latency cloud services