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 |
|---|---|---|
| Member of Technical Staff - Software Engineer (SuperIntelligence team) This role focuses on building and operating the core platform infrastructure for training, evaluating, and deploying large-scale AI models within Microsoft. It involves designing scalable services for cluster orchestration, job scheduling, data pipelines, and artifact management, with a strong emphasis on production operations, cloud platforms (Azure), and enhancing developer experience for AI research and engineering teams. | Serve | 7 |
| Member of Technical Staff, Hardware Health - MAI Superintelligence Team This role is focused on ensuring the reliability, performance, and availability of Microsoft's large-scale AI training infrastructures, which involve tens of thousands of GPUs and advanced networking. The responsibilities include designing transport, fabric architecture, telemetry, observability, and automated troubleshooting for these clusters. The role also involves AI training and inference cluster bring-up, performance benchmarking, and root-cause analysis, with a goal of developing predictive health models and autonomous remediation systems. |
| Serve |
| 7 |
| Research Intern - AI System Architecture Modeling and Performance Research Intern role focused on AI system architecture modeling and performance within Azure's hyperscale infrastructure. The intern will evaluate hardware/software co-design opportunities, optimize CPU, GPU, and networking infrastructure for AI accelerators, and develop methodologies for performance analysis and architectural idea evaluation. | Serve | 7 |
| Research Intern - AI Hardware Research Intern role focused on AI Hardware, specifically on chip architectures for efficient AI serving and inference systems. The role involves research, analysis, documentation, and innovation in collaboration with researchers and engineers. | Serve | 7 |
| Research Intern - AI Frameworks (Network Systems and Tools) Research intern focused on next-generation AI systems, specifically exploring disaggregated inference, memory-architecture, and interconnect technologies for LLM serving, with a focus on request scheduling and KV caching optimizations. The role involves investigating and evaluating disaggregated KV cache architectures and building a P2P service KV cache sharing architecture. | Serve | 7 |
| Technical Program Manager - Infrastructure Technical Program Manager for AI Infrastructure at Microsoft AI, focusing on building and optimizing platforms for large-scale foundation model training, deployment, and serving. The role involves coordinating projects, collaborating with researchers and engineers, and driving progress in a 0->1 environment. | ServePost-train | 7 |
| Senior Researcher - Systems and Networking, Microsoft Research Senior Researcher in Systems and Networking at Microsoft Research, focusing on AI-driven methods for system innovation, performance, efficiency, and scalability. The role involves developing and implementing new methodologies, collaborating with cross-functional teams, and publishing research findings. Requires a Doctorate and background in systems/networking with knowledge of ML systems, databases, and networking technologies, including Agent Systems and Vector Databases. | Serve | 7 |
| 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 |
| Research Intern - Azure Research - Systems Research Intern role focused on next-generation cloud and AI systems, with a focus on improving efficiency, reliability, and usability of Microsoft's online services and datacenters. Projects include efficient GPU/LLM deployments, AIOps, and serverless computing. The role involves research, prototyping, evaluation, and potential publication. | Serve | 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 |