Principal Software Engineer - Pro-Code Server Engineer

Microsoft•Mountain View, CA

19h•$142,800 - $304,200

About The Position

Innovation & Technology Programs (ITP), within Microsoft's Office of the CTO and MCAPS-Core, develops and operates technology for Microsoft's Customer Service & Support and Customer Success Unit businesses. ITP brings together product, engineering, marketing, sales, customer success, and partners to accelerate customer outcomes and business growth. Its engineering organization focuses on innovation, production, and shared technology operations. The Principal Software Engineer – Pro-Code Server Engineer is a hands-on technical leader responsible for improving pro-code engineering across ITP. The role leads critical work on AI-enabled platforms while mentoring engineers, defining standards, building tools, and prototyping solutions to difficult engineering challenges. A major focus is modernizing mature, long-lived codebases while integrating generative AI, agents, retrieval, and intelligent automation. This requires understanding existing architecture and behavior, managing dependencies, preserving compatibility, reducing technical debt, and establishing repeatable approaches that teams can adopt without disrupting customers. As a Principal-level individual contributor, the engineer writes and reviews production code, prototypes solutions to reduce architectural risk, and develops standards and golden paths for effective pro-code development. The role promotes tools, including AI-assisted engineering tools, to increase productivity and is accountable for durable architecture, quality, security, privacy, accessibility, Responsible AI, and long-term developer experience. Candidates need recent, substantial hands-on production software experience, success working in and modernizing large, mature, or legacy codebases, and a record of improving other engineers' effectiveness through mentoring, standards, and tooling. Experience focused mainly on low-code platforms, configuration, prompt engineering, or proofs of concept does not meet the core requirements. A bachelor's degree in computer science or a related technical field and at least six years of technical engineering experience coding, or equivalent experience, is required. Microsoft cannot sponsor a work visa for this role. The position requires passing Microsoft's Cloud Background Check upon hire or transfer and every two years thereafter, along with any applicable Microsoft, customer, or government security screenings.

Requirements

Bachelor's degree in computer science or a related technical field and 6+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Recent, substantial hands-on experience developing production software; experience limited primarily to low-code platforms, solution configuration, prompt engineering, or proof-of-concept development does not meet the role's core requirements.
Experience working within and modernizing large, mature, or legacy codebases.
Track record of multiplying other engineers' pro-code effectiveness through mentoring, standards, and tooling.
Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
Microsoft is unable to sponsor a work visa for this role due to the nature of the job duties.

Nice To Haves

Master's degree in computer science or a related technical field and 8+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python; or a bachelor's degree in the field and 12+ years of such experience; or equivalent experience.
Extensive experience designing, building, shipping, and operating large-scale server, cloud, or distributed software systems.
Deep pro-code experience, including substantial recent hands-on work writing, reviewing, debugging, testing, and operating production software in languages such as C++, C#, Java, Go, Rust, or Python.
Experience tracing unfamiliar execution paths, understanding historical architecture, managing complex dependencies, and successfully modernizing large, mature, or legacy codebases while maintaining service availability and compatibility.
Experience defining engineering standards and best practices and driving adoption across multiple teams.
Experience building or significantly improving developer tools, build and test infrastructure, or engineering automation.
Experience rapidly prototyping solutions to ambiguous, technically difficult problems and turning them into production-quality designs.
Experience with production engineering practices, including test automation, continuous integration, safe deployment and rollback, logging and telemetry, incident response, performance analysis, experimentation, and secure development.
Ability to apply security, privacy, safety, accessibility, and Responsible AI requirements throughout the software development lifecycle.
Experience communicating complex technical concepts and influencing engineering and business leaders across organizational boundaries.
Expertise in server architecture, distributed systems, concurrency, storage, networking, API design, or high-scale cloud services.
Experience building production AI systems using large language models, retrieval-augmented generation, agent orchestration, tool integration, model gateways, prompt management, or AI evaluation.
Experience integrating AI capabilities into established enterprise products, rather than developing only greenfield demonstrations or prototypes.
Experience creating architectural seams and migration strategies for gradual modernization of tightly coupled systems.
Experience applying AI-assisted engineering tools to understand, test, document, and safely improve large codebases.
Knowledge of Responsible AI and AI safety practices, privacy, security, accessibility, compliance, and data-governance requirements for enterprise AI systems.
Demonstrated ability to improve engineering effectiveness, code health, reliability, and developer experience across a broad organization.
Experience building internal developer platforms, golden paths, code analyzers, or AI-assisted developer tools.
Experience leading engineering guilds, communities of practice, or technical training programs.
Strength in systems thinking, architecture design, resilient design, algorithm design, automation, data-driven problem solving, strategic technical implementation, communication, and negotiation.

Responsibilities

Write production code, review critical changes, debug complex failures, and model pro-code craftsmanship.
Mentor engineers on design, code quality, testing, debugging, secure coding, performance, and AI-assisted development through reviews, pairing, talks, and communities of practice.
Develop, publish, update, and drive adoption of pro-code standards covering coding conventions, reviews, test automation, CI/CD, observability, secure and Responsible AI development, and AI-assisted coding; measure outcomes such as code health, defect escape rate, and lead time.
Develop a deep understanding of mature and legacy codebases, including historical design decisions, compatibility requirements, undocumented dependencies, and operational constraints.
Define and share repeatable modernization approaches, including architectural seams, strangler patterns, characterization tests, incremental refactoring, dependency untangling, and managed migrations; turn lessons into playbooks and reference implementations.
Identify developer workflow friction and drive creation of tools, automation, templates, analyzers, and AI-assisted agents and skills for code comprehension, test generation, migration, refactoring, review, and release.
Prototype solutions to difficult engineering problems, prove feasibility, surface risks, and provide delivery teams or product groups with clear designs and reference code.
Design and build scalable, resilient, secure, and cost-effective services, APIs, orchestration layers, agent runtimes, retrieval systems, and evaluation frameworks while balancing performance, reliability, maintainability, cost, and backward compatibility.
Diagnose and resolve complex cross-system issues involving concurrency, distributed state, dependencies, performance, data quality, and model behavior.
Establish safe deployment, progressive exposure, rollback, recovery, monitoring, and end-to-end testing practices for critical production changes.
Use telemetry, incident learnings, and quality indicators to measure and improve engineering effectiveness, including the velocity and quality impact of AI-assisted development.