Senior/principal Software Engineer - Growth (coreai)

Microsoft Microsoft · Big Tech · Mountain View, CA +4 · Software Engineering

This role focuses on building and scaling AI-powered growth systems for Microsoft's core AI products like GitHub Copilot and VS Code. The engineer will design, ship, and iterate on growth experiments, improve A/B testing frameworks, and use data to drive decisions, aiming to accelerate user adoption and engagement with AI features.

What you'd actually do

  1. Own growth through engineering excellence and experimentation
  2. Design, ship, and iterate on growth experiments across acquisition, activation, engagement, and retention
  3. Build and evolve A/B testing frameworks, metrics, and tooling that raise experimentation quality and confidence
  4. Partner closely with Product, Data Science, Design, and Research to turn hypotheses into shipped learnings
  5. Use data and telemetry to guide decisions — from experiment design to rollout strategy

Skills

Required

  • Software engineering fundamentals
  • experience building, shipping, and operating production services or client applications
  • Proficiency in designing scalable, reliable systems that support rapid iteration and experimentation
  • Experience writing high-quality, maintainable code and owning services end-to-end in a live environment
  • Ability to reason about performance, reliability, and correctness in high-traffic systems
  • Experience supporting or driving experimentation (A/B testing) in production environments
  • Understanding of experimentation quality, including guardrails, metrics selection, and rollout strategies
  • Engineering mindset grounded in hypothesis-driven development and learning through iteration
  • C, C++, C#, Java, JavaScript, or Python

Nice to have

  • 2+ years shipping A/B tests end-to-end in production (design → instrumentation → analysis → rollout), including improving experiment quality and guardrails
  • 4+ years building and shipping high-availability features for multi-region, globally distributed systems (resiliency, failover, capacity planning)
  • Demonstrated experience leaning heavily on AI to accelerate engineering velocity, including using AI tools to prototype, implement, debug, and iterate on production features

What the JD emphasized

  • shipping and scaling code
  • high-traffic, global AI products
  • shipping A/B tests end-to-end in production
  • high-availability features for multi-region, globally distributed systems

Other signals

  • AI-first engineering systems
  • shipping and scaling code that shapes developer adoption of AI
  • AI-powered growth loops
  • high-velocity, AI-first engineering organization
  • hundreds of millions of users discover, adopt, and succeed with AI
  • responsible growth in the AI organisation