Principal Software Engineer - Growth (coreai)

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

Principal Software Engineer focused on building AI-first growth and experimentation systems for Microsoft's CoreAI organization, which powers products like GitHub Copilot. The role involves architecting and implementing foundational engineering systems for measuring, experimenting with, and scaling AI experiences across Microsoft products, with a strong emphasis on engineering excellence, reliability, and driving measurable business outcomes.

What you'd actually do

  1. Own growth through engineering excellence and experimentation — at a systems level
  2. Architect and build paved paths for online experimentation: standardized metrics, guardrails, analysis workflows, and rollout automation that improve reliability and decision quality across teams
  3. Lead multi‑workstream initiatives that span teams/products (e.g., unified growth measurement, cross‑surface funnels, experimentation quality improvements)
  4. Build and evolve core capabilities: telemetry foundations, experiment assignment/targeting, feature flighting, and risk controls (kill‑switches, guardrails, progressive delivery)
  5. Partner with Product, Data Science, Design, and Research to turn ambiguous goals into shippable, measurable systems

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
  • Ability to meet Microsoft, customer and/or government security screening requirements

Nice to have

  • 8+ years building and shipping high-availability features for multi-region, globally distributed systems (resiliency, failover, capacity planning)
  • 4+ years of experience in experimentation with emphasis on quality: guardrails, bias detection, metric integrity, rollout risk management, and enterprise grade experimentation
  • Demonstrated experience building experimentation/measurement platforms (feature flags/flighting, assignment, metrics pipelines, analysis tooling) used by multiple teams
  • Demonstrated experience leaning heavily on AI to accelerate engineering velocity, including using AI tools to prototype, implement, debug, and iterate on production systems
  • Cross-team influence: you create clarity, align stakeholders, and drive execution across organizational boundaries — without becoming a “piece‑mover”

What the JD emphasized

  • AI-first growth and experimentation systems
  • scale across Microsoft
  • foundational engineering systems
  • ship, measure, learn faster
  • technical strategy for how we measure, experiment, and scale AI experiences
  • building repeatable systems
  • raising the engineering bar
  • experiment quality
  • telemetry
  • safer rollouts
  • clearer business outcomes
  • pragmatic execution
  • deep technical judgment
  • CoreAI – Platform and Tools
  • end‑to‑end Copilot & AI stack
  • powering products like GitHub Copilot and Visual Studio Code
  • empower every developer to shape the future with AI
  • accelerating how AI applications are built, deployed, and improved at global scale
  • growth through engineering excellence and experimentation — at a systems level
  • Architect and build paved paths for online experimentation
  • standardized metrics
  • guardrails
  • analysis workflows
  • rollout automation
  • improve reliability and decision quality across teams
  • Lead multi‑workstream initiatives that span teams/products
  • unified growth measurement
  • cross‑surface funnels
  • experimentation quality improvements
  • Build and evolve core capabilities
  • telemetry foundations
  • experiment assignment/targeting
  • feature flighting
  • risk controls (kill‑switches, guardrails, progressive delivery)
  • Partner with Product, Data Science, Design, and Research
  • turn ambiguous goals into shippable, measurable systems
  • Stay close to the work
  • write production code
  • review designs/PRs
  • coach others through architecture and implementation tradeoffs
  • Proven ability to design and ship systemic solutions (platforms, frameworks, primitives)
  • enable multiple teams to move faster
  • deeply hands‑on and detail‑oriented
  • Software engineering fundamentals
  • building, shipping, and operating production services or client applications at scale
  • Demonstrated technical leadership across architecture, service fundamentals, and quality (reliability, performance, operability, security, and maintainability)
  • Track record of translating ambiguous product/business problems into measurable engineering outcomes
  • clear hypotheses
  • instrumentation
  • decision criteria
  • High data aptitude
  • comfortable with metrics, telemetry, and experimental analysis
  • raise the bar on experimentation rigor and interpretation
  • 8+ years building and shipping high‑availability features for multi‑region, globally distributed systems (resiliency, failover, capacity planning)
  • 4+ years of experience in experimentation with emphasis on quality: guardrails, bias detection, metric integrity, rollout risk management, and enterprise grade experimentation
  • Demonstrated experience building experimentation/measurement platforms (feature flags/flighting, assignment, metrics pipelines, analysis tooling) used by multiple teams
  • Demonstrated experience leaning heavily on AI to accelerate engineering velocity, including using AI tools to prototype, implement, debug, and iterate on production systems
  • Cross‑team influence
  • create clarity
  • align stakeholders
  • drive execution across organizational boundaries