Principal Software Engineer - Pro-Code Server Engineer

Microsoft•Mountain View, CA

19h

About The Position

Innovation & Technology Programs (ITP) builds and continually reinvents technology for Microsoft's Customer Service & Support and Customer Success businesses. Within the Office of the CTO, ITP Engineering develops and operates technology that helps customers achieve value faster. The team works alongside ITP's strategy, delivery, and productivity teams and is organized around Innovation, Production, and shared Tech Ops functions. The Principal Software Engineer – Pro-Code Server Engineer is a hands-on technical leader responsible for critical, high-impact AI-enabled platforms and raising pro-code engineering effectiveness across ITP. The role involves writing and reviewing production code, prototyping solutions to difficult engineering problems, mentoring engineers, defining development standards, and building or promoting developer tools, including AI-assisted tools. A central focus is understanding and modernizing mature, long-lived codebases while integrating generative AI, agents, retrieval, and intelligent automation. The engineer will navigate existing architecture, dependencies, compatibility needs, and technical debt, and establish repeatable approaches that teams can adopt without disrupting customers. The role is accountable for durable architecture, quality, security, privacy, accessibility, Responsible AI, and long-term developer experience. The position requires substantial recent hands-on production software experience, success working in and modernizing large, mature, or legacy codebases, and a record of multiplying others' pro-code effectiveness through mentoring, standards, and tooling. Experience primarily limited to low-code platforms, solution configuration, prompt engineering, or proof-of-concept work does not meet the core requirements. The typical U.S. base pay range is USD $142,800–$274,800 per year. For specific work locations in the San Francisco Bay Area and New York City metropolitan area, the range is USD $188,000–$304,200 per year. Certain roles may be eligible for benefits and other compensation. The position will be open for a minimum of five days, with applications accepted on an ongoing basis until filled. Microsoft cannot sponsor a work visa for this role. The role requires passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

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.
Demonstrated success working within and modernizing large, mature, or legacy codebases.
Track record of improving others' pro-code effectiveness through mentoring, standards, and tooling.
Ability to meet Microsoft, customer, and/or government security screening requirements, including 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.
Extensive experience designing, building, shipping, and operating large-scale server, cloud, or distributed software systems.
Deep pro-code experience writing, reviewing, debugging, testing, and operating production software in one or more languages such as C++, C#, Java, Go, Rust, or Python.
Experience tracing unfamiliar execution paths, understanding historical architecture, managing complex dependencies, and modernizing codebases while preserving 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.
Ability to communicate complex technical concepts and influence engineering and business leaders across organizational boundaries.
Deep expertise in server architecture, distributed systems, concurrency, storage, networking, API design, or high-scale cloud services.
Experience creating architectural seams and migration strategies that enable 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, privacy, security, accessibility, compliance, and data-governance requirements for enterprise AI systems.
Strength in systems thinking, architecture design, resilient design, algorithm design, automation, data-driven problem solving, strategic technical implementation, communication, and negotiation.

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 equivalent experience.
Bachelor's degree in Computer Science or a related technical field and 12+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
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 only greenfield demonstrations or prototypes.
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.

Responsibilities

Write production code, review critical changes, debug complex failures, and set an example for pro-code craftsmanship.
Mentor engineers across ITP 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, testing, CI/CD, observability, secure and Responsible AI development, and AI-assisted coding; measure adoption and outcomes.
Build a deep understanding of mature and legacy codebases, including historical 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; produce playbooks and reference implementations.
Identify developer workflow friction and drive tools, automation, templates, analyzers, and AI-assisted agents and skills that improve code comprehension, testing, migration, refactoring, review, and release.
Prototype solutions to difficult engineering problems, demonstrate feasibility, surface risks, and provide designs and reference code to delivery teams or product groups.
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 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 improve engineering effectiveness, including the velocity and quality impact of AI-assisted development.