Microsoft Store team is focused on ensuring that Windows users at home, work, and school find it easy to discover and engage with the experiences they need most and that Independent Software Vendors (ISVs) are excited to build Windows experiences because they can innovate, differentiate, and exceed their business goals on Windows.
Requirements
- Proven experience coding in languages including, but not limited to, C, C++, C-Sharp, Java, JavaScript, or Python
- Experience in application development for Windows or other platforms.
- Experience with conversational memory systems and agentic AI concepts.
- Familiarity with at least one AI orchestration framework (e.g., LangChain, Semantic Kernel, MCP).
- Experience integrating AI models into applications via APIs or SDKs.
- Experience with LLM-based agent systems or recommendation systems (retrieval, ranking, personalization).
- Solid analytical skills and ability to interpret experiment results.
Responsibilities
- Contribute to the development and maintenance of the Windows client application and related components used by millions of users worldwide.
- Deliver a performant and fluid experience that delights customers and enables them to discover and install the apps, games, and content that matter most to them as quickly and easily as possible.
- Deepen your understanding of app development and the Windows UI stack, while working on high-impact areas such as performance, personalization, and intelligent discovery in the Microsoft Store experience.
- Leverage LLMs and recommendation systems to build features that make the Store smarter, faster, and more relevant for our users.
- Grow your engineering skills through hands-on contributions to critical platform components and accelerate your career growth by collaborating with experienced engineers, applied scientists, and data experts to ship AI-powered, high-quality software at scale.
- Partner closely with project management, applied science, data, and design teams to define opportunities where artificial intelligence (AI) and large language models (LLMs) can improve user experience.
- Implement, test, and optimize features, ensuring low latency and high performance in AI-driven scenarios.
Other
- Collaborating across design, program management, and data disciplines.
- Solving complex and ambiguous challenges.
- Participate in experiment design and execution to evaluate model performance, feature impact, and customer satisfaction.
- Break down complex initiatives into actionable engineering tasks with realistic delivery estimates.
- Communicate complex technical and experimental findings clearly to both technical and non-technical stakeholders.
- Ensure AI and data usage complies with responsible AI and privacy principles.
- Experience collaborating with applied science or ML research teams.
