Principal Software Engineer

Microsoft Microsoft · Big Tech · United States · Software Engineering

Principal Software Engineer focused on hardware strategy and performance architecture for Microsoft 365 AI experiences like Copilot and Agents. The role involves defining benchmarking, workload characterization, and performance evaluation frameworks, and building AI-assisted engineering capabilities for performance analysis and optimization. It bridges distributed systems, AI workloads, hardware, and software performance.

What you'd actually do

  1. Drive hardware strategy and hardware-software co-design for Microsoft 365, Copilot, Agents, PAC, and Substrate workloads.
  2. Define benchmarking, workload characterization, and performance evaluation frameworks used to influence platform and infrastructure decisions across compute, gpu, memory, storage, networking, and accelerator technologies.
  3. Lead deep technical investigations into performance, efficiency, and scalability challenges across large-scale distributed systems and AI-powered services.
  4. Partner with Azure, infrastructure, and platform engineering teams to evaluate emerging hardware technologies and translate platform capabilities into meaningful customer and business impact.
  5. Build the next generation of AI-assisted performance engineering capabilities, enabling faster analysis, diagnosis, optimization, and decision-making across Microsoft 365 services.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience
  • 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
  • Bachelor's Degree in Computer Science or related technical field AND 15+ years technical engineering experience
  • Proven track record leading performance, systems, infrastructure, or hardware strategy initiatives for large-scale cloud services.
  • Deep understanding of distributed systems, computer architecture, performance engineering, benchmarking, and workload analysis.
  • Experience using data, experimentation, and technical analysis to influence platform, architecture, or infrastructure investment decisions.
  • Demonstrated ability to lead cross-organizational technical efforts and influence senior engineering leaders.
  • Experience applying AI to engineering workflows, performance analysis, diagnostics, optimization, or operational decision-making.
  • Experience with AI infrastructure, large-scale inference workloads, retrieval systems, agent platforms, or Copilot-style architectures.
  • Experience evaluating emerging hardware platforms and translating hardware capabilities into product and platform advantages.
  • Experience building AI-assisted engineering systems for diagnostics, performance analysis, optimization, or operational intelligence.

What the JD emphasized

  • hardware strategy
  • performance evaluation frameworks
  • AI-assisted performance engineering capabilities
  • large-scale distributed systems
  • AI-powered services
  • emerging hardware technologies
  • AI infrastructure
  • large-scale inference workloads
  • agent platforms
  • Copilot-style architectures

Other signals

  • AI-assisted performance engineering
  • hardware strategy for AI workloads
  • performance evaluation frameworks for AI services