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 - AI Frontiers Research Intern position at Microsoft AI Frontiers Lab focusing on advancing agentic model capabilities. The role involves creating models and agents that can perform tasks across digital systems using automation, reasoning, and interaction, leveraging both text and visual environments. The research includes areas like reinforcement learning for reasoning and tool use, novel training algorithms, synthetic environment creation, multi-agent training, scaling laws, and optimization techniques. The role also involves working with various data modalities and modern model architectures, with a focus on publishing in top-tier venues and potentially shipping AI technologies in products. | AgentPost-train | 10 |
| Applied Scientist- II Applied Scientist II role focused on fine-tuning frontier LLMs for enterprise data, enabling task-specific agents and solutions. The role involves research in LLM post-training, particularly reinforcement learning for long-horizon dynamic workflows, and driving research into product capabilities for M365 Copilot. |
| Post-trainAgent |
| 9 |
| Senior Researcher - Efficient AI Applied research role focused on advancing efficiency across the AI stack for large-scale generative AI experiences in Microsoft 365. The role involves optimizing AI serving systems from algorithmic and systems levels down to hardware and kernel optimizations, with a focus on end-to-end ownership from research to production deployment. | Serve | 9 |
| Principal Applied Researcher Principal Applied Researcher to lead research projects on AI for developers, taking them from research into product. This role involves collaborating with scientists, engineers, and designers, building and training state-of-the-art models, applying and advancing LLMs for software engineering (including RAG and evaluation), and running experiments on a large scale with developer product teams. | ShipPost-train | 9 |
| Research Intern - Self-Improving AI Research Intern position focused on self-improving AI systems, involving language modeling and reinforcement learning. The role is part of Microsoft Research NYC and requires enrollment in a PhD or master's program with experience in deep learning, language modeling, and/or reinforcement learning. Preferred qualifications include experience with deep learning frameworks like PyTorch and Hugging Face Transformers. | Post-trainAgent | 9 |
| Principal Researcher - Agentic AI - Microsoft Research AI Frontiers Principal Researcher focused on expanding AI capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms. The role involves developing self-improving, adaptive agents for dynamic enterprise environments, integrating pre-training, post-training, RL, multi-agent collaboration, and deployment. Key responsibilities include research on recursive self-improvement, continual learning, human-AI collaboration, and building robust evaluation frameworks and synthetic data pipelines. | AgentData | 9 |
| Senior Researcher - Agentic AI - Microsoft Research AI Frontiers Research Scientist focused on agentic AI, developing self-improving, multi-agent systems that learn through interaction. The role involves research on recursive self-improvement, adaptive coordination, continual learning, human-AI collaboration, and autonomous skill acquisition. It spans the research-to-deployment spectrum with a focus on building evaluation frameworks and synthetic data pipelines, and refining architectures for scalability. The work is grounded in foundation models and learning agent platforms, with potential for product impact and publication. | AgentData | 9 |
| Senior Principal Researcher - Microsoft Research AI Frontiers Senior Principal Researcher at Microsoft Research AI Frontiers focused on pushing the boundaries of agentic AI, including large language and multimodal models, reasoning, and multi-agent systems. The role involves leading research, developing training methodologies, curating datasets, designing evaluation frameworks, and prototyping new agentic capabilities, with opportunities for publication and real-world impact. | AgentPost-train | 9 |
| Researcher - Multiple Levels, Microsoft Research AI Frontiers Research role focused on pushing the boundaries of agentic AI, large language, and multimodal models. The role involves developing cutting-edge training methodologies, curating datasets, designing evaluation frameworks, and prototyping new agentic capabilities. Emphasis on research, publication, and potential for real-world impact. | PretrainAgent | 9 |
| Principal Researcher - Artificial Specialized Intelligence - Microsoft Research Principal Researcher in Artificial Specialized Intelligence focusing on developing cutting-edge large foundation models and post-training techniques for specific domains and scenarios. The role involves integrating AI systems, developing research prototypes, and publishing findings in top-tier venues. | PretrainPost-train | 9 |
| Senior Researcher - Artificial Specialized Intelligence - Microsoft Research Senior Researcher at Microsoft Research focusing on Artificial Specialized Intelligence, developing cutting-edge large foundation models and post-training techniques for specific domains and real-world applications. The role involves conducting research, collaborating with cross-functional teams, developing prototypes, and publishing findings. | PretrainPost-train | 9 |
| Research Intern - Foundations of GenAI Research Intern role focused on advancing Generative AI and Large Language Model Technologies, contributing to the development and exploration of LLMs and Multimodal AI models. The lab's mission is to expand AI capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms, with research areas including reasoning, new architectures, action models, multi-agent systems, and evaluation. | PretrainAgent | 9 |
| Research Intern - Machine Learning and Optimization Research Intern position focused on the intersection of Machine Learning, Optimization, and Large Language Models (LLMs) for efficient decision-making. Projects involve training LLMs for algorithm design, accelerating optimization algorithms, and using LLMs for sequential decision-making. The role involves designing algorithms/models, prototyping, conducting experiments, and potentially contributing to publications. | PretrainAgent | 9 |
| Research Intern - LLM Performance Optimization Research Intern role focused on optimizing the performance of Large Language Models (LLMs), involving architecture and inference performance. Requires PhD student status in a STEM field and experience with LLM architecture or inference performance optimization. Preferred qualifications include experience with GPU kernel performance bottlenecks and optimizing compiler architecture. | Serve | 9 |
| Research Software Engineer - Multiple Levels- AI Frontiers Research Software Engineer focused on Generative AI and agentic applications within Microsoft Research's AI Frontiers lab. The role involves developing, improving, and exploring capabilities of AI models and agentic systems, with a focus on creating reliable digital workers that can execute entire workflows and collaborate with humans and other agents. Key areas include enhancing agent reasoning, improving robustness, reimagining workflows for an agent-native world, and creating end-to-end experiences. | AgentData | 9 |
| Research Intern - Diagnostic Imaging AI, Imaging Computing, Reconstruction and Inverse Problem Research Intern role focused on AI for medical image reconstruction and analysis. Involves developing new imaging AI model architectures, training methods, solving inverse problems for image reconstruction, and analyzing model performance including artifacts and hallucinations. Requires PhD enrollment, deep learning for imaging experience, and publication record. | Post-trainData | 9 |
| Member of Technical Staff - Machine Learning (AI Team) Research role focused on creating LLM models for general purpose capabilities and products, with a focus on agentive applications and advancing AI towards controllable, safety-aligned superintelligence. Responsibilities include developing new training methods, data collection, evaluation, and building user-facing features. | AgentPost-train | 9 |
| Research Intern - AIP AI Knowledge Multimodal AI Research Intern role focusing on multimodal AI, specifically the synergy between vision and language. The intern will explore LLMs, SLMs, and VLMs for tasks like video understanding, document analysis, and multi-page QA, with hands-on experience in leveraging LLMs for document understanding, grounding, and retrieval-based generation. The role involves prototyping, experimenting, and publishing research. | Post-trainAgent | 9 |
| Research Intern - Deep Learning Group Research Intern position in Microsoft's Deep Learning group focusing on cutting-edge research in deep learning, AI, and related fields. The intern will collaborate with researchers, work on challenging problems, and potentially publish their findings. The role requires a PhD student with experience in Machine Learning, Deep Learning, and mathematical modeling. | Pretrain | 9 |
| Senior Researcher - AI Agents - Microsoft Research Senior Researcher focused on advancing state-of-the-art in agentic systems and ecosystems, including building agent-native ecosystems, developing advanced agentic capabilities through post-training methods, creating novel human-agent interaction techniques, and developing evaluation benchmarks and tooling for agents. The role emphasizes outcome-driven innovation, collaborative innovation, problem-solving, decision-making, and the scientific method, with a strong publication policy and potential for shipping AI technologies. | AgentPost-train | 9 |
| Senior Researcher - Artificial Specialized Intelligence, Microsoft Research Senior Researcher in Artificial Specialized Intelligence focusing on developing cutting-edge large foundation models and post-training techniques for specific domains and scenarios. The role involves conducting research, collaborating with cross-functional teams, developing prototypes, and publishing findings. | Post-trainPretrain | 9 |
| Research Intern - Microsoft Research Special Projects Research intern focused on advancing Large Language Model (LLM) capabilities, specifically in areas like memory representations, retrieval methods (e.g., GraphRAG), handling temporal changes, ambiguity, and hallucination detection/mitigation. | Post-trainAgent | 9 |
| Research Intern - Machine Learning and Optimization - Redmond Research intern position focusing on the intersection of Machine Learning and Optimization, specifically combining Large Language Models (LLMs) with optimization for efficient decision-making. Projects involve training LLMs for algorithm design, accelerating optimization algorithms, and using LLMs for sequential/distributed decision-making. The role involves designing algorithms/models, prototyping, conducting experiments, and analyzing results, with potential contributions to papers. | Post-trainAgent | 9 |
| Research Intern - Model Optimization and HW Acceleration Research Intern role focusing on optimizing LLMs, VLMs, and generative models, with an emphasis on hardware acceleration. The role involves designing experiments, developing algorithms, and collaborating on practical solutions, aiming to advance AI technologies. | Post-trainServe | 9 |
| Research Intern - Multimodal Language Models Research intern to explore efficient multimodal language models using techniques like model compression, quantization, and optimization for resource-constrained platforms. Focus on training strategies for vision-language tasks, prototype implementation, experiment design, and result analysis. | Post-trainServe | 9 |
| Research Intern - Fundamentals of AI: Security, Agents, Systems & Control Research Intern role focused on advancing core AI research in security, agentic workflows, and next-generation architectures. Key areas include strengthening AI robustness, designing post-training alignment strategies for multi-turn interactions, and investigating novel architectures beyond transformers. The role involves fundamental research and system-level applications, leveraging shared resources and evaluation frameworks. | Post-trainAgent | 9 |
| Research Intern - AI Frontiers - Reasoning & Agentic Models Research intern role focused on advancing agentic model capabilities, including reasoning, tool use, and end-to-end workflow execution across text and visual environments. The role involves research in reinforcement learning, novel training algorithms, synthetic environment creation, multi-agent training, and scaling laws, with a focus on foundational models and various data modalities. The internship aims to contribute to cutting-edge research and potentially ship AI technologies in products. | AgentPost-train | 9 |
| Research Intern - Artificial Intelligence Research Intern position at Microsoft Research focusing on artificial intelligence projects including multimodality, world models, LLMs, and computer vision. The role involves conducting research, developing novel methodologies, and disseminating findings through publications. Requires enrollment in a master's or PhD program. | Pretrain | 9 |
| Research Intern - Systems for Reliable and Scalable AI Agents Research Intern position focused on building reliable, safe, and efficient AI agents. The role involves exploring formal verification, reliability, safety, intelligent task handling, secure execution, and efficiency optimization for AI agents. It is a research-focused role within Microsoft Research, aiming to tackle complex challenges in AI agent systems. | Agent | 9 |
| Research Intern - Machine Learning at MSR NYC Research Intern at Microsoft Research NYC focusing on machine learning and artificial intelligence, with specific interests in reinforcement learning for language models, science of deep learning, post-training, reasoning, and test-time scaling. The role involves collaborating with researchers, presenting findings, and contributing to research projects over a 12-week internship. | Post-train | 9 |
| Technical Solution Management Specialist - AI Evaluation, Research, and Technical Architecture Func. This role focuses on establishing and operating an AI Innovation Lab within Microsoft's Customer Experience and Success organization. The primary responsibility is to research, validate, and evaluate early-stage AI opportunities (pre 0-to-1) before they are handed off for prototyping or product engagement. This involves defining research frameworks, building evaluation approaches for various AI workloads, developing automated testing, and creating reference architectures. The role acts as a bridge between business strategy, research, and technical execution, ensuring AI innovations are rigorously tested and aligned with Responsible AI standards. | Eval GateData | 8 |
| Senior Applied Scientist and Principal Applied Scientist This role focuses on developing and industrializing deep learning speech technologies, with a specific emphasis on integrating speech with LLMs for multimodal modeling. The scientist will invent new algorithms, apply them to real products, and evaluate their performance against guardrails, impacting millions of users. | Post-trainServe | 8 |
| AI for Science Postdoctoral Researcher - Biomolecular AI & Experimental Data Integration Postdoctoral Researcher focused on integrating experimental biological data with machine learning models for biomolecular simulation and drug discovery. The role involves designing and scaling experimental datasets, developing methods to connect ML models with experimental observables, and creating closed-loop workflows between models and experiments. | Data | 8 |
| Applied Researcher 2/ Senior Applied Researcher Applied Researcher role focused on post-training code specific models and agentic research for developer tools like Github Copilot and VS Code. The role involves building and managing large-scale ML experiments, creating new datasets, and collaborating with product teams to take research from concept to product. | Post-trainAgent | 8 |
| Principal Applied Scientist - CoreAI The Principal Applied Scientist will develop machine learning techniques for safety, alignment, and trustworthy AI, collaborating across teams to build and maintain responsible AI systems from development to production. This role focuses on the application and improvement of AI models, particularly large language models, within Microsoft's CoreAI organization. | Post-train | 8 |
| Senior Researcher - Machine Learning - Microsoft Research Senior Researcher in Machine Learning within Microsoft Research's Health Futures organization, focusing on advancing AI for biomedicine and life sciences discovery. The role involves designing, implementing, and evaluating novel AI methodologies, including post-training, inference-time optimization, and interpretability techniques. | Post-trainServe | 8 |
| Senior Researcher - Machine Learning for Life Sciences - Microsoft Research Senior Researcher in Machine Learning for Life Sciences at Microsoft Research, focusing on advancing AI for biomedicine and life sciences discovery. The role involves designing, implementing, and evaluating novel AI methodologies, including post-training, inference-time optimization, interpretability, and experimental design. It also requires application-specific benchmarking, interpretation of deep learning models on biological data, and developing approaches for inference-time optimization. | Post-trainServe | 8 |
| Research Intern - Applied Speech Research Research Intern role focused on applied speech research within the Health and Life Sciences group at Microsoft. The internship involves leveraging AI and speech technologies to improve healthcare outcomes and operations, with a focus on speech recognition, synthesis, and understanding. The role is for PhD candidates in Computer Science or related STEM fields. | Post-train | 8 |
| Research Intern - Agentic Programming Research Intern role focused on agentic programming, involving building agentic workflows and ML models for software engineering tasks, within a PhD program context. | Agent | 8 |
| Research Intern - Applied Sciences Group Research intern to investigate Small Language Model (SLM) architectures and techniques, such as recurrent transformers and universal transformers, for maximizing LLM throughput with limited cache on hardware targets like SoCs, GPUs, or NPUs. Will involve model training at scale using Azure compute and collaboration with a multidisciplinary team. | Post-trainServe | 8 |
| Member of Technical Staff - Data Research Engineer - MAI Superintelligence Team Seeking Data Research Engineers to join the Multimodal team, focusing on designing and curating high-quality datasets for next-generation foundation models across vision, language, and audio. The role involves developing data collection strategies, improving dataset quality, analyzing data-driven model behaviors, and building tools for dataset auditing, all within the context of responsible AI practices. | Data | 8 |
| Research Intern - AI/ML Numerics & Efficiency Research Intern role focusing on ML systems, numeric precision, data types, and compute technologies for AI workloads at Azure scale. The role involves investigating model efficiency through low-precision formats, quantization, ML kernel development, and benchmarking. It aims to inform decisions on compute platforms, acceleration strategies, and system-level optimizations for training and inference of large-scale models. | Serve | 8 |
| Research Intern - AI Systems & Architecture Research internship focused on AI systems and architecture, investigating performance modeling, architectural analysis, and emerging inference mechanisms for large-scale AI workloads. The role involves analyzing hardware, software, and model interactions, developing performance models, and prototyping new inference techniques. | Serve | 8 |
| Research Intern - STAC, NYC (Sociotechnical Alignment Center) Research Intern position at Microsoft's Sociotechnical Alignment Center (STAC) focusing on evaluating AI systems, particularly generative ones. The role involves applying measurement theory from social sciences and statistics to assess risks, capabilities, and performance. Collaboration with Fairness, Accountability, Transparency, and Ethics in AI (FATE) researchers is expected. The internship emphasizes theoretical and methodological approaches to advance AI system evaluation. | Eval Gate | 8 |
| Research Intern - LLM Acceleration Research intern focused on accelerating large language models (LLMs) by optimizing performance on custom architectures, involving computer architecture and parallel programming. | Serve | 8 |
| Research Intern - Computer Vision and Deep Learning Research Intern position focused on computer vision and deep learning to improve user understanding of the environment and human-computer interactions. Involves analyzing multimodal sensor data and pushing the state of the art in the field. | Post-train | 8 |
| Applied Sciences INTERN Internship role focused on analyzing and improving advanced algorithms, implementing AI prototypes, and preparing data for machine learning purposes. The role involves working with large-scale datasets and contributing to the creation of intelligent solutions. | Data | 7 |
| Principal Researcher, Office Product Group Principal Researcher role within the Office Product Group at Microsoft, focusing on advancing AI-powered experiences embedded in Microsoft 365 applications. The role involves designing, developing, evaluating, and deploying AI systems that can reason and act across tools and documents, with a strong emphasis on scaling these capabilities to a large user base. Key responsibilities include developing frameworks for AI component coordination, improving code generation, implementing fine-tuning and post-training for LLMs, designing evaluation strategies for real-world workflows, and collaborating to translate research into production-ready features. | AgentPost-train | 7 |
| Applied Science: Microsoft AI Internship Opportunities - Redmond This internship focuses on applying AI/ML expertise to real-world product scenarios within Microsoft's AI Content and Commerce, Search Fundamentals, and Search Place teams. Interns will work on problems in search, personalization, NLP, computer vision, and recommendation systems, translating research into production-ready solutions for products like Copilot. | Ship | 7 |
| Principal Applied Scientist This role focuses on building intelligence for the advertising marketplace, understanding user behavior, measuring impact, and optimizing outcomes. It involves developing large-scale learning systems for intent inference and causal effects from noisy data, influencing ranking, bidding, pricing, and budget allocation. The role requires defining and driving the scientific and technical strategy for data-driven attribution and causal measurement, establishing methodologies for incrementality estimation, counterfactual learning, and bias correction, and leading the production adoption of relevant frameworks. The ideal candidate will have deep expertise in causal inference and a track record of delivering measurable business impact with ML systems. | Ship | 7 |