Microsoft has 521 active AI-related job listings. The majority of these roles are focused on agents, representing 37% of the total, followed by application and serving infrastructure. Engineering is the most frequent function, with a significant number of openings, and the United States is the primary hiring country. Frequent tech tags include agent orchestration, model serving, and LLM observability, suggesting a focus on operationalizing AI models. Over the last 30 days, Microsoft has added 280 new AI roles, a 157% increase compared to the previous 30-day period.
Currently tracking 250 active AI roles, down 24% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$331k (avg $195k).
Microsoft currently has 343 active AI-related roles in our index. The most common open titles are: Principal Software Engineer (19), Senior Software Engineer (19), Software Engineer II (8), Principal Applied Scientist (7), Principal Data Scientist (4). Most positions are in Engineering and Research.
Microsoft's active AI hiring is concentrated in: agents (36%), application (21%), serving infrastructure (19%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Microsoft is hiring AI talent in: United States (308 roles), Canada (15 roles), Japan (8 roles), United Kingdom (7 roles).
Job postings at Microsoft most frequently mention: Computer Architecture, Python, Machine Learning, C#, C++.
In the past 30 days, Microsoft has posted 227 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Research Intern - Hardware/Software Codesign Research intern focused on advancing the efficiency of AI systems through hardware/software codesign, exploring novel designs and optimizations across the AI stack, including models, frameworks, cloud infrastructure, and hardware. The role involves practical implementation skills for efficient, scalable computational kernels and aims to contribute to mid- and long-term product innovations. | Serve | 7 |
| Research Intern - AI Hardware Research Intern role focused on AI Hardware, specifically researching chip architectures for efficient AI serving and inference systems. Collaborates with researchers to increase performance and efficiency of cutting-edge inference systems. | Serve | 7 |
| Serve |
| 7 |
| Research Intern - Gray Systems Lab (GSL) Research intern role focusing on machine learning for systems, reinforcement learning, natural language-to-query, and agentic workflows within Azure Data's Gray Systems Lab. The internship involves research and development for databases, big-data, and cloud systems, with potential impact on production systems and publications. | Post-trainAgent | 7 |
| Research Intern - MSR Software-Hardware Co-design Research intern role focused on pioneering technologies for AI/ML workloads, specifically improving efficiency, security, and robustness of GPU memory systems, agentic AI systems, and software architecture for hardware accelerators. The role involves fast-paced execution, implementation, and evaluation on Azure platforms, with a focus on systems and AI. | Serve | 7 |
| Research Intern - Reliability of Cloud and AI Systems Research intern role focused on applying LLM and Agentic technology to improve the reliability of large-scale cloud and AI systems. The role involves analyzing production data, designing and building novel tools for monitoring and troubleshooting, and validating solutions on real Microsoft services. | AgentServe | 7 |
| Research Intern - RiSE group Research Intern role in the RiSE group focusing on ML Systems, AI workloads, agentic workflows, and ML models for software engineering tasks. Requires PhD program enrollment and expertise in related STEM fields, with preferred qualifications in ML systems, AI, and software engineering. | Data | 7 |
| Software Engineer II - CoreAI Software Engineer II - CoreAI role focused on developing connectors and enriching ingested content with AI/ML capabilities for Azure AI Search, aiming to make it the knowledge and memory layer for Agentic applications. This involves generating vector embeddings, transforming data, and advancing multi-modal search. | AgentData | 7 |