Much of the platform is built on mature, long-lived codebases. A central challenge is modernizing them while integrating generative AI, agents, retrieval, and intelligent automation. That means understanding existing architecture and behavior, untangling complex dependencies, preserving compatibility, and reducing technical debt. It also means setting up repeatable modernization approaches that other teams can adopt without disrupting customers.
As a Principal-level individual contributor, you will stay close to the code. You will write and review critical production code. You will prototype solutions to hard problems so teams can reduce risk before they commit to an architecture. You will develop the standards and golden paths that define effective pro-code development in ITP, and build or promote tools, including AI-assisted engineering tools, that make engineers more productive. You will be accountable for durable architecture, quality, security, privacy, accessibility, Responsible AI, and the long-term developer experience.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Responsibilities
Pro-code leadership: Stay deeply hands-on: write production code, review critical changes, debug complex failures, and set the example for pro-code craftsmanship.
Mentoring: Mentor engineers across ITP on effective pro-code practice, covering design, code quality, testing, debugging, secure coding, performance, and AI-assisted development. Do this through code and design reviews, pairing, tech talks, and communities of practice.
Standards: Develop, publish, and update standards for effective pro-code development. These cover coding conventions, review practices, test automation, CI/CD, observability, secure and Responsible AI development, and AI-assisted coding. Drive adoption and measure it (code health, defect escape rate, lead time).
Legacy understanding: Build a deep understanding of mature and legacy codebases, including historical design decisions, compatibility requirements, undocumented dependencies, and operational constraints.
Modernization approaches: Define and share repeatable modernization approaches: architectural seams and strangler patterns, characterization tests, incremental refactoring, redesigning untestable code, untangling dependencies, and carefully managed migrations rather than unnecessary rewrites. Turn the lessons learned into playbooks and reference implementations.
Tooling: Find friction in the developer workflow and drive the creation of tools, automation, templates, analyzers, and AI-assisted agents and skills. These should speed up code comprehension, test generation, migration, refactoring, review, and release.
Prototyping: Prototype solutions to the hardest software engineering problems, such as novel architectures, performance and scale limits, AI integration patterns, and modernization strategies. Prove feasibility, surface risks, and hand off clear 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. Balance new capabilities against 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.
To be successful in this role: In this pro-code engineering role, candidates have recent, substantial hands-on experience developing production software, demonstrated success working within and modernizing large, mature, or legacy codebases, and a track record of multiplying the pro-code effectiveness of others through mentoring, standards, and tooling. Experience limited primarily to low-code platforms, solution configuration, prompt engineering, or proof-of-concept development will not meet the core requirements of this role.
Qualifications
Required/minimum qualifications
Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
Other requirement
Microsoft is unable to sponsor a work visa for this role due to the nature of the role’s job duties.
Ability to meet Microsoft, customer and / or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire / transfer and every two years thereafter.
Additional or preferred qualifications
Master'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 Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
Extensive experience designing, building, shipping, and operating large-scale server, cloud, or distributed software systems.
Deep, extensive pro-code experience, including substantial recent hands-on work writing, reviewing, debugging, testing, and operating production software in one or more languages such as C++, C#, Java, Go, Rust, or Python.
Proven experience working in large, mature, or legacy codebases (tracing unfamiliar execution paths, understanding historical architecture, managing complex dependencies), with concrete, successful approaches to modernizing them while keeping services available and compatible.
Experience defining engineering standards and best practices and driving their adoption across multiple teams.
Experience building or significantly improving developer tools, build and test infrastructure, or engineering automation.
Experience quickly prototyping solutions to ambiguous, technically hard 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.
Demonstrated 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.
Deep 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 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 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.
Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
- Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
- https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
