Senior Software Engineer

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

Senior Software Engineer role focused on scaling Windows Engineering System's autonomous "autopilot" systems for AI-scale vulnerability discovery and proofing. The role involves building AI evals, scaling pipeline throughput and reliability, managing compute and AI infra, and optimizing costs for large-scale distributed systems.

What you'd actually do

  1. AI Evals & Benchmarks: Building AI evals and benchmarks to ensure that the system stays healthy and does not regress across multiple scenarios.
  2. Throughput & Reliability: Scaling the pipeline by an order of magnitude and improve reliability.
  3. Compute, Capacity & Orchestration: Managing compute and AI infra at Windows-wide scale; smoothing capacity spikes across model-hosting backends; managing queuing/scheduling and pool prioritization infrastructure.
  4. Cost & Efficiency: Driving billing/consumption estimation and cost-of-goods modeling.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.
  • Ability to meet Microsoft, customer and/or government security screening requirements

Nice to have

  • Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience.
  • Experience building or operating AI agent / LLM-based systems at production scale.
  • Hands-on experience with Azure DevOps (ADO) pipelines and CI/CD automation.
  • Experience with Microsoft Azure cloud infrastructure (compute, capacity planning, cost-of-goods).
  • Comfortable working with C# and Microsoft technologies, including hands-on experience with Microsoft Copilot CLI, Azure DevOps (ADO) pipelines and CI/CD automation.
  • Demonstrated experience designing and operating large-scale distributed systems or cloud service backends (orchestration, scheduling, autoscaling, or multi-tenant compute).

What the JD emphasized

  • AI-discovered vulnerabilities
  • autonomous systems
  • AI-scale volume
  • multi-model scanning harness
  • AI agent / LLM-based systems at production scale

Other signals

  • AI-discovered vulnerabilities
  • autonomous systems
  • AI-scale volume
  • multi-model scanning harness
  • AI agent / LLM-based systems at production scale