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Overview
We are seeking a Principal Software Engineer to help redefine how engineering gets done across Windows Silicon and System Integration (WSSI) and demonstrate what agentic engineering can make possible for Windows at scale.
The Windows Silicon and System Integration (WSSI) organization delivers a leading integrated silicon + OS platform for the new era of computing. Our work spans new and in-market devices, multiple form factors and silicon partners, and some of the hardest systems challenges in Windows, including performance, power, reliability, compatibility, and overall product quality. We believe agentic AI can fundamentally change how engineers build, validate, debug, and improve complex systems.
As a Principal Software Engineer, you will set technical direction for agentic engineering across WSSI while remaining deeply engaged with the technology. You will identify the engineering problems where agents can have the greatest impact, architect and build solutions alongside teams, and take promising ideas from prototype to trusted production workflows.
In this role, you will help turn WSSI's most successful agentic capabilities into a compelling technical story: demonstrating what is possible, sharing lessons and reusable approaches, building excitement and credibility with engineering leaders, and establishing WSSI as a source of innovation in agentic engineering across Windows + Devices and Microsoft. As those capabilities mature, you will identify opportunities to extend them to new engineering scenarios and partner organizations, creating new areas where WSSI technology and capabilities can have broader impact.
This role therefore sits at the intersection of deep engineering, technical strategy, and organizational influence. You will bring together AI, engineering systems, telemetry, diagnostics, and developer workflows to create closed-loop experiences that help engineers find problems faster, understand root causes, develop solutions, and validate outcomes. At the same time, you will articulate the vision behind that work, engage technical leaders and partner teams, and create momentum around opportunities that can grow from successful WSSI solutions into broader engineering capabilities.
This is not a role solely focused on introducing new AI tools. You will instead define how engineering itself evolves as agentic capabilities mature, while also shaping the role WSSI plays in that evolution. You will establish reusable technical patterns, raise the engineering bar for AI-enabled systems, influence teams across organizational boundaries, and build the mechanisms that allow successful approaches to scale while expanding the impact and reach of WSSI's agentic engineering capabilities.
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
- Set the technical vision and multi-year strategy for agentic engineering across WSSI, defining how agents can transform design, implementation, code review, testing, validation, debugging, diagnostics, and release workflows.
- Lead from architecture through production. Identify the most impactful engineering problems, establish the technical approach, build critical components where needed, and lead focused cross-functional AI squads from experimentation through evaluation, deployment, adoption, and continuous improvement.
- Pioneer closed-loop agentic engineering systems that combine telemetry, engineering data, tools, and reasoning to detect issues, investigate likely causes, automate diagnostic and debugging workflows, propose and implement solutions, and verify outcomes with appropriate human oversight.
- Establish the technical architecture for agentic workflows at scale, including reusable approaches for orchestration, context engineering, tool integration, evaluation, observability, reliability, and safe operation in complex engineering environments.
- Turn technical breakthroughs into organizational momentum. Build compelling demonstrations and technical narratives around WSSI's agentic capabilities, communicate their impact to engineering and technical leadership, and represent WSSI's work across Windows + Devices and Microsoft.
- Create the engineering bar for production AI systems. Define rigorous evaluation methods, quality standards, failure-mode analysis, safeguards, and operational practices that enable teams to move from compelling demos to dependable engineering workflows.
- Measure real engineering impact. Establish outcome measures and feedback loops across engineering productivity, product quality, workflow reliability, developer experience, and sustained adoption, using evidence to guide technical investment and continuously improve deployed systems.
- Drive technical alignment across organizational boundaries. Build consensus around architecture and engineering standards, influence technical leaders without direct authority, and create durable approaches that teams across WSSI can adopt and extend.
- Establish rigorous standards for responsible AI, security, privacy, compliance, and human oversight, ensuring agentic systems earn engineers' trust and can be safely integrated into critical engineering workflows.
- Multiply technical capability across WSSI. Mentor engineers and technical leaders, develop communities of practice, share reusable patterns and lessons, and help teams build the technical proficiency to create and operate agentic workflows independently.
- Stay ahead of a rapidly evolving field. Maintain deep awareness of relevant industry and research advances, determine which developments matter for WSSI, and translate them into concrete technical recommendations, experiments, and durable engineering improvements.
- Identify opportunities to expand WSSI's impact. Recognize where technology, architectures, and workflows developed within WSSI can address broader engineering problems, cultivate partnerships around those opportunities, and help shape new areas of technical investment and ownership.
Qualifications Required Qualifications:
- Bachelor's Degree in Computer Science, Information Technology, or related field AND 8+ years technical experience in software engineering, network engineering, service engineering, or systems engineering + OR equivalent experience.
Other Requirements:
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.
Preferred Qualifications:
- Experience using modern AI-assisted engineering tools and capabilities in software development workflows, including one or more of design, coding, testing, validation, diagnostics, debugging, or operations.
- Experience leading focused cross-functional teams or AI squads to deliver complex technical initiatives across team or organizational boundaries.
- Experience moving agentic projects beyond prototypes into production deployment or sustained multi-team adoption, with measurable improvements in productivity, quality, reliability, or developer experience.
- Experience architecting production agentic systems, including orchestration, tool use, prompt and context engineering, retrieval, evaluation, observability, reliability, cost, latency, and integration into existing engineering environments.
- Experience delivering telemetry-driven issue detection, validation, automated diagnostics or debugging, code remediation, and closed-loop verification at scale.
- Experience in one or more of operating system development, driver development, performance and power analysis, responsible AI, security.
#WINDOWS, #W+D, #WSSI
Software Engineering IC6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 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 $220,800 - $331,200 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.
Salary Context
This $165K-$331K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Microsoft, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($248K) sits 17% above the category median. Disclosed range: $165K to $331K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Microsoft AI Hiring
Microsoft has 62 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, Research Scientist. Positions span New York, NY, US, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia, Redmond, WA, US. Compensation range: $128K - $331K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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