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About This Role
Overview
Project Sophia is a new generation business application, built ground up from market\-disruptive, AI\-first product principles. The product has been designed to completely re\-define how complex, cross domain business problems are solved. Every interaction, every component and every user experience has been designed leveraging the full power and potential of generative AI and is taking the notion of AI based experiences way beyond the standard AI chat experiences.
Multi\-modal concepts, combined with an emphasis of visual appeal and ease of use, is empowering any business user to interact with an infinite, AI Powered Business Research canvas where users can explore and resolve any business question both within and across business domains. Powered by large language models and deep business domain expertise, Project Sophia will empower the user to build out research journeys, automatically suggesting relevant data sources and correlating insights, intelligently visualizing exploration outcomes and suggesting next actions in the research journey. Project Sophia represents the new way of doing business, enabling strategic business decision making in a fraction of the time – filling a gap in the market where no app exists today.
As we advance our product journey, we are innovating and iterating at a rapid pace. To further accelerate our momentum by bringing agentic, AI\-powered experiences to the business application space and transforming how people work we continue to expand our investments in generative AI capabilities.
The Team
We are a small, but very dynamic and passionate team that fully embraces the build\-measure\-learn iterative development approach. We work towards the common goal of building a product customers love. As a natural part of our product design and development process we are working very closely with customers as well as listening to telemetry and other key signals which allow us to consistently evolve based on customer demand and adoption patterns. Deep collaboration across PM, Design, Engineering, and Science is a given and all disciplines play important roles as part of the product development process.
In Sophia’s user experience team, we are committed to building the best\-in\-class AI experiences to gain insight into strategic business questions. We foster a culture of diversity, collaboration, and innovation, valuing everyone's unique perspectives and contributions. Our agile, start\-up\-like environment encourages out\-of\-the\-box thinking and empowers each team member to contribute towards our shared mission of delivering a highly innovative, agentic, AI powered user experience that is positioned to fundamentally change what business applications look like in the future.
The Candidate Profile \& Role
As a Principal Software Engineer\- AI on the team, you will bring deep software engineering and AI expertise to architect, design, and deliver intelligent solutions and end\-to\-end experiences for Sophia. You’ll collaborate with business and technology leaders, internal users, and partners to shape technical direction and build scalable, production\-ready systems that leverage AI to solve complex business requests.
In this role, you are expected to lead across software engineering fundamentals—architecture, coding, testing, and deployment—while pioneering AI\-centric UI and delivering differentiated, AI\-native experiences that set Sophia apart in the market.
We are seeking individuals who are customer\-focused, passionate, creative, and collaborative, with a proven track record of delivering impactful and delightful experiences at scale. If you are motivated by our mission to innovate in AI\-driven UI and define novel interaction paradigms, we encourage you to apply.
Joining Project Sophia offers a unique opportunity to work on something highly strategic to Microsoft and be at the cutting edge of innovation. We provide opportunities for technical leadership, mentorship, and career growth within a dynamic and fast\-paced environment.
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
As a Principal Software Engineer\- AI with applied AI focus, you will apply both software engineering and AI expertise to build intelligent, scalable solutions. You’ll collaborate with cross\-functional teams to deliver high\-impact features aligned with enterprise standards and cloud\-scale requirements. Your responsibilities include:
- Design, implement, and ship AI\-first product capabilities end\-to\-end from rapid prototype to production, spanning LLM\-powered services, retrieval/grounding pipelines, and intelligent UX experiences that delight users through Sophia’s AI canvas.
- Shape and evolve full\-stack architecture, integrating front\-end experiences, back\-end services, and AI orchestration layers that connect models, context, and tools to deliver cohesive, extensible, high\-performance systems.
- Collaborate with design, research, and platform teams to adapt or fine\-tune LLMs/SLMs and multimodal models for real\-world customer scenarios, ensuring outcomes are contextual, transparent, and human\-centered.
- Build agentic, tool\-using, and multimodal workflows that reason across data and services; optimize for safety, latency, reliability, and cost efficiency.
- Drive engineering excellence, including secure\-by\-design principles, accessibility compliance, automated testing, and high\-quality code craftsmanship across the product lifecycle.
- Define and apply evaluation frameworks for AI systems, using telemetry, experimentation, and continuous feedback loops to improve reasoning quality and user experience.
- Drive live\-site reliability and operational excellence, participating in On\-Call rotations while maintaining a sustainable, high\-ownership engineering culture.
Qualifications Required 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 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:
- BS/MS in Computer Science or equivalent or relevant industry experience.
- 8\+ years of experience with modern web technologies such as .NET / Node / React / Angular, building RESTful APIs.
- Solid experience in an OO language such as C\#, Java, or Python.
- Experience working with generative AI, large language models, or agent\-based systems in production scenarios.
- Proven track record of designing and delivering complex, scalable systems.
- Excellence in one or more general programming languages including but not limited to: Python, C\#; JavaScript; TypeScript.
- Comfortable driving complex server \& client architecture across large product teams.
- Hands\-on experience with modern LLM evaluation techniques, including LLM\-as\-a\-Judge, agentic evaluations, and RAG assessments.
- A track record of delivering successful, large\-scale applied ML projects in an industry setting.
- Experience with MLOps practices, including model versioning, automated testing, monitoring, and CI/CD for machine learning.
- Cloud \& Infrastructure: Skilled in building and operating infrastructure using Azure, AWS, or Google Cloud, and deploying containerized models with Docker, Kubernetes, or similar tools.
- Solid coding, debugging, and problem\-solving skills, with a focus on system\-level thinking.
\#BICJOBS
\#CXAJOBS
Software Engineering IC5 \- The typical base pay range for this role across the U.S. is USD $139,900 \- $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.
Salary Context
This $139K-$304K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $189K across 518 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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% 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 $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($222K) sits 6% below the category median. Disclosed range: $139K to $304K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Microsoft AI Hiring
Microsoft has 49 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span Redmond, WA, US, San Francisco, CA, US, Washington, DC, US. Compensation range: $159K - $331K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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