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 |
|---|---|---|
| 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 |
| Researcher Intern Applied Researcher Intern role focusing on AI for developers, specifically post-training code-specific models and agentic research for tools like Github Copilot. The role involves building and training state-of-the-art models, applying LLMs to software engineering tasks, and running large-scale experiments. | Post-train |
| 9 |
| AI for Science Internship - Machine Learning Intern AI for Science Internship - Machine Learning Intern at Microsoft Research AI for Science, focusing on developing next-generation foundational AI capabilities for materials design. The role involves contributing to research on efficient and expressive ML models, working with domain experts, and preparing technical papers. Experience with generative models and agent-driven research is preferred. | Post-trainAgent | 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 Product Manager, AI Model Security Product Manager for AI Model Security within Microsoft's Superintelligence Team, focusing on hardening frontier LLMs against security threats (prompt injection, jailbreaking, data exfiltration, etc.) and ensuring models deliver capabilities for real-world security workflows. This role involves defining the security roadmap, driving exploit defense, building red-teaming frameworks, partnering with security product teams, and shaping launch readiness, with a strong emphasis on understanding attacker perspectives and balancing capability with risk. | Post-trainAgent | 9 |
| Research SDE II Research SDE II at Microsoft Research India focusing on advancing AI/ML innovation, including new models, scalable architectures, systems support, and large-scale applications. The role involves working closely with researchers and engineers, driving end-to-end research and development, and potentially mentoring interns. Requires strong coding and engineering skills, comfort with open problems, and a proven track record in AI/ML. | Post-trainServe | 9 |
| Technical Advisor, Microsoft Superintelligence (Office of the CEO) Technical Advisor to the CEO of Microsoft AI, focusing on Humanist Superintelligence. The role involves providing strategic counsel, diving deep into research problems (model evaluation, prototyping, technical tradeoffs), and synthesizing insights for executive decision-making. It spans model development, infrastructure, safety, and alignment, requiring a blend of technical expertise and strategic advisory. | Post-trainServe | 9 |
| Member of Technical Staff - Data Scientist Data Scientist role focused on building next-generation post-training methods for frontier models at Microsoft AI. Responsibilities include designing evaluations, producing high-quality training data, building scalable data pipelines, and running post-training experiments to improve model capabilities like instruction following, coding, and agentic behaviors. The role operates across the full post-training lifecycle, from data generation to reward modeling and reinforcement learning, with a focus on turning raw model capability into reliable and measurable performance improvements. | Post-trainData | 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 - Post-Training This role focuses on post-training methods for AI models, including continual pre-training, large-scale deep reinforcement learning, and data synthesis. The team works on language and multimodal models, with a focus on code-specific applications like Github Copilot. They also develop data infrastructure, tooling, and conduct research to improve model performance and impact. The role involves designing datasets, advancing model training, and ensuring data quality for cutting-edge AI. | Post-trainData | 9 |
| Member of Technical Staff - Post-Training This role focuses on advancing post-training methods for AI models, including continual pre-training, large-scale deep reinforcement learning, and fine-tuning. The team works on both OpenAI and open-source models, develops data infrastructure for large datasets, and creates tooling for dataset auditing. They also integrate language and multi-modality for products like Github Copilot and Visual Studio Code, with a mission to push the boundaries of AI toward Humanist Superintelligence. | Post-trainData | 9 |
| Member of Technical Staff - Machine Learning (AI Team) This role focuses on creating and improving LLM models for general-purpose capabilities and products, with a strong emphasis on agentive applications and safety alignment. The responsibilities include developing new training methods, collecting data, evaluating LLMs, building data flywheels, creating tooling for training/evals, writing production code, and developing new user-facing features. The role involves working on core LLM capabilities, fine-tuning, and training classifiers to support Microsoft products and APIs, with a specific focus on advancing towards 'Humanist Superintelligence' that is controllable and safety-aligned. | Post-trainAgent | 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 |
| Member of Technical Staff - Machine Learning (AI Team) This role focuses on developing and improving LLM models for general-purpose capabilities and products, with a strong emphasis on agentive experiences. Responsibilities include data acquisition/generation, generalizing ML solutions, leading evaluation efforts, adapting state-of-the-art research, and writing production-quality code. The role involves fine-tuning, training classifiers, and engineering prompts, aiming to push the boundaries of AI towards controllable, safety-aligned superintelligence. | Post-trainAgent | 9 |
| Research Intern - Office of Applied Research Research intern position focusing on Generative AI, LLMs, NLP, Reinforcement Learning, and Human-Centered AI. The role involves research in areas like collaborative AI agents, multi-agent simulations, tool-use, learning from user feedback, synthetic data generation, LLM evaluation, and model fine-tuning. Requires a PhD program enrollment and research publication experience. | Post-trainAgent | 9 |
| Research Software Development Engineer Microsoft Research India is seeking research engineers with a demonstrated track record in AI/ML, including new models, scalable ML architectures, systems support for such workloads, and innovative large-scale applications of ML models. The role involves working closely with researchers and engineers, comfortable with open problems and changing requirements, and driving end-to-end research and development from ideation to deployment. | Post-trainServe | 9 |
| Research Engineer - Generative AI - AI Frontiers Research Engineer role at Microsoft Research's AI Frontiers lab focused on advancing AI capabilities in modeling, algorithms, reasoning, and agentic AI. The role involves developing novel ideas in reinforcement learning, evolving pre-training, mid-training, and post-training codebases for LLMs, and contributing to reasoning and agentic AI research. Opportunities to ship research to Microsoft customers. | Post-trainAgent | 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 - Training Methods for LLM Efficiency Research intern to design and apply training algorithms for improving the quality/efficiency trade-offs of large language models, focusing on resource-constrained environments. Potential research directions include quantized model fine-tuning, improving token efficiency for reasoning models, and scaling training under resource constraints. | Post-trainData | 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 - 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 |
| Software Engineer II Software Engineer II role on the Azure AI Foundry Customization team, focused on building and scaling the AI platform for Azure and Microsoft's flagship products. The role involves developing pre-training, mid-training, and post-training solutions, working with LoRA models, and handling inference at scale. Responsibilities include creating abstractions, infrastructure, and features for training, testing, validation, scaling, and optimization of ML algorithms, as well as driving customer-inspired innovations and ensuring code quality and security. | Post-trainServe | 8 |
| Principal Applied Scientist Principal Applied Scientist role at Microsoft AI Web Data team focused on building the data foundation for Bing and Microsoft AI experiences, including large-scale grounding and LLM training. The role involves translating research into production, advancing state-of-the-art modeling, and deploying algorithms and solutions to improve system performance, quality, data management, and accuracy. | Post-trainPretrain | 8 |
| Senior Software Engineer Senior Software Engineer role on the Foundry Agents team at Microsoft CoreAI, focusing on building a platform for Azure AI customization. The role involves end-to-end ownership of systems that enable AI to learn, improve, and scale in production, including pre-training, mid-training, and post-training solutions, LoRA model deployment, and large-scale inferencing. Responsibilities include developing ML algorithms, infrastructure, and features for training, testing, validation, scaling, and optimization, with a focus on making agentic AI economically viable for enterprises. | Post-trainServe | 8 |
| Applied Scientist II Applied Scientist II at Microsoft AI focusing on Generative AI and Agentic Modeling for consumer products like Bing and Copilot. The role involves building and optimizing production ML models, working with SOTA generative models, analyzing large-scale data, designing experiments, and delivering insights for business decisions. Requires expertise in ML, Generative AI, Agentic Modeling, or Data Science, with hands-on experience with LLMs/SLMs. | Post-trainAgent | 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 |
| Principal Applied Scientist This role focuses on building and leading the development of advanced multilingual speech models, AOAI finetuning, and multimodal generative AI for Microsoft's Azure Speech team. The goal is to create transformative speech technologies for voice agents, transcription, and call centre analytics, impacting billions of users globally, with a special focus on India. The role involves setting technical direction, driving innovation, scaling model quality, and delivering breakthrough technologies. | Post-trainServe | 8 |
| Senior Applied Scientist Senior Applied Scientist role focused on developing and shipping innovative machine learning solutions, particularly large language models and conversational search experiences, for Microsoft products. Responsibilities include training models, creating evaluation sets and infrastructure, fine-tuning LLMs, and driving projects from design to implementation and shipping. The role involves deep data analysis, prompt engineering, and setting up engineering pipelines. Experience with production systems and end-to-end AI product development is emphasized. | Post-trainServe | 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 |
| Senior Applied Scientist Senior Applied Scientist role focused on developing advanced multilingual speech models, LLM speech finetuning, and multimodal generative AI for real-time transcription, intelligent voice agents, and multilingual speech solutions within Microsoft's Azure Speech team. The role involves setting technical directions, building novel data generation solutions, and collaborating with global teams to deliver impactful speech technologies for Microsoft products and enterprise solutions, with a focus on India. | Post-trainAgent | 8 |
| Machine Learning Scientist II This role focuses on researching and developing machine learning models, specifically large language models (LLMs), for Microsoft 365 Copilot. The core responsibilities involve advancing research through projects, developing new algorithms and prototypes, and collaborating with product groups and Microsoft Research. The role emphasizes fine-tuning models for specific applications and contributing to the AI platform infrastructure that enables Copilot workflows. The position requires expertise in LLMs, deep learning, and responsible AI, with a strong emphasis on shipping applied research to production. | Post-trainServe | 8 |
| Applied Scientist II Applied Scientist II role at Microsoft focusing on integrating AI research into productivity products like M365 Copilot. The role involves defining and leading research projects, collaborating with cross-functional teams, and contributing to the scientific community through publications. Research areas include reinforcement learning, agentic systems, multimodal modeling, post-training techniques, and LLM applications. | Post-trainAgent | 8 |
| Principal Applied Scientist Principal Applied Scientist role at Microsoft AI Web Data team, focusing on building the data foundation for Bing and Microsoft AI experiences. This involves large-scale grounding, LLM training, and end-to-end processing of web content. The role translates research into production, advancing state-of-the-art modeling and deploying algorithms to improve system performance and accuracy. | Post-trainPretrain | 8 |
| Senior Director, Design - Model Experience Seeking a Design leader to own the end-to-end design quality of AI model personality, tone, and user-facing experiences. This player-coach role involves hands-on IC work (80%) and coaching a small team (20%) of model and UX designers. Responsibilities include defining the creative vision, shaping model personality, driving UX for product surfaces, and building evaluation systems for design quality. The role requires a strong design craft, intuition for language and personality, and the ability to ship regularly. | Post-trainShip | 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 |
| Research Intern - AI Safety and Security Research intern position focused on LLM safety and security, involving research into protecting LLMs from malicious inputs and using LLMs for computer security. The role involves collaboration with researchers and contributing to research publications. | Post-trainAgent | 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 |
| 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. The role involves building and managing ML experiments, creating datasets, and collaborating with product teams to apply and advance LLM approaches for software engineering, including RAG and evaluation, with opportunities to ship AI advancements to millions of developers. | Post-trainAgent | 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 - 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 |
| Applied Scientist - Core AI Speech Research role focused on developing and industrializing novel speech algorithms, including integrating speech with LLMs for multimodal modeling, and applying these models to real products with continuous monitoring and evaluation. | Post-train | 8 |
| Senior Applied Scientists and Principal Applied Scientists (Multiple Positions) - Copilot Tuning Seeking Senior/Principal Applied Scientists to fine-tune LLMs on tenant data for M365 Copilot, creating task-specific agents and solutions. Role involves writing training pipelines, designing experiments, implementing inference solutions, and shipping models to customers. Focus on advancing LLM capabilities in an enterprise context. | Post-trainServe | 8 |
| Applied Scientist II / Senior Applied Scientist - Responsible AI (CoreAI) The role focuses on building and scaling Responsible AI service components, specifically involving supervised fine-tuning of LLMs with RLHF, conducting evaluations, and developing agent adversarial evaluations and safety mitigations. The goal is to enable customers to use AI responsibly and securely. | Post-trainAgent | 8 |
| Principal Machine Learning Engineer Principal Machine Learning Engineer for Health Futures team focused on accelerating training of generative models, advancing model capabilities, and optimizing training/evaluation/inference pipelines for health and life sciences applications. | Post-trainServe | 8 |
| Member of Technical Staff - Post Training - MAI Superintelligence Team This role focuses on the post-training of large language models (LLMs) to improve their capabilities in areas like reasoning, instruction following, math, code, and tool use. The responsibilities include data collection, building evaluations, and applying advanced reward modeling and RL techniques. The goal is to advance the state-of-the-art in LLM performance and contribute to the development of superintelligent AI systems. | Post-train | 8 |