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About This Role
Overview
At Microsoft Edge, part of Microsoft's AI Experiences organization, delivering a fast, reliable, and high\-quality browser depends on solid engineering fundamentals. Edge is playing an increasingly strategic role as the platform where millions of users experience AI\-powered capabilities like Copilot every day. We are looking for a Senior Software Engineer to join the Edge Fundamentals Team and help drive the quality, performance, reliability, and engineering effectiveness that powers those experiences at scale.
The problems you'll tackle span browser performance, diagnostics, AI\-powered developer experiences, engineering productivity, telemetry, experimentation, and automation. No two projects are the same, but all are focused on improving Microsoft Edge and how it is built. You will build systems that help engineers understand software behavior, identify issues earlier, make better decisions, and move faster with confidence.
You will work on some of the hardest engineering problems in Edge. One project might focus on building an AI\-assisted diagnostic experience that helps engineers understand a performance regression in minutes instead of days. Another might focus on improving how quality and performance are measured across thousands of code changes and experiments. In other cases, the right solution may be a targeted improvement directly in the Edge or Chromium codebase that delivers measurable benefit to customers.
We believe AI is transforming how software is designed, developed, tested, and maintained. Successful candidates will embrace AI as both a tool and a platform, using it to improve engineering outcomes while helping shape the next generation of tools and experiences that make developers more effective.
Success in this role is measured by your ability to improve Microsoft Edge. Whether it is helping teams catch issues earlier, making engineering decisions more data\-driven, accelerating development through AI and automation, or delivering improvements that customers can directly see and feel, your work will help raise the quality, performance, and reliability of Edge.
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.
Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50\- mile commute of a designated Microsoft office in the U.S. or 25\-mile commute of a non\-U.S., country\-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
Responsibilities
- Design, implement, and maintain software that improves the quality, performance, reliability, and engineering effectiveness of Microsoft Edge, whether through product improvements, diagnostics, automation, measurement systems, or developer tooling.
- Own significant engineering investments from design through deployment, including implementation, validation, monitoring, and long\-term maintenance.
- Build diagnostics, telemetry, automation, experimentation, and evaluation systems that help engineers better understand software behavior and make data\-driven decisions.
- Develop and apply AI\-powered solutions that improve engineering workflows, accelerate investigations, reduce manual effort, and help teams move faster with confidence.
- Build tools and experiences that help engineers detect issues earlier, diagnose problems faster, and improve the efficiency of software development across Edge.
- Analyze telemetry, traces, experiments, and other signals to identify opportunities to improve both engineering systems and customer experiences.
- Partner across teams to improve how quality, reliability, performance, and engineering productivity are measured, evaluated, and improved.
- Contribute to technical investigations and, when appropriate, develop product fixes that improve customer\-facing quality and performance.
- Modernize existing systems and workflows to improve scalability, maintainability, reliability, and developer productivity.
- Participate in technical design discussions, code reviews, and mentoring while helping raise the technical bar across the team.
Qualifications Required Qualifications:
- Bachelor's Degree in Computer Science or related technical field AND 4\+ years technical engineering experience with coding in languages including, but not limited to, C\#, Python, C\+\+, or Powershell
+ 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:
- Master'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\#, Python, C\+\+, or Powershell
+ OR 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\#, Python, C\+\+, or Powershell
+ OR equivalent experience.
- Experience designing and shipping high\-quality software at scale.
- Experience with browser technologies, performance engineering, diagnostics, developer productivity, distributed systems, operating systems, or large\-scale engineering infrastructure.
- Experience using telemetry, experimentation, profiling, tracing, or performance analysis to drive engineering decisions.
- Experience building tools, automation, measurement systems, CI/CD infrastructure, diagnostics platforms, or engineering productivity solutions.
- Demonstrated experience using AI\-assisted engineering tools to improve software design, implementation, testing, debugging, diagnostics, and developer productivity.
- Experience building AI\-powered workflows, developer experiences, copilots, agents, evaluation systems, or other AI\-enabled solutions.
- Demonstrated ability to independently drive technical work from ambiguous problem statements through implementation and delivery.
- Solid collaboration and communication skills with the ability to work effectively across multiple engineering teams.
Software Engineering IC4 \- The typical base pay range for this role across the U.S. is USD $119,800 \- $234,700 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 $160,200 \- $261,000 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 $119K-$261K range is above the median for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Microsoft, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $119K to $261K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Microsoft AI Hiring
Microsoft has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span US, CA, US, Redmond, WA, US. Compensation range: $147K - $331K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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