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.

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.

This role focuses on building and scaling the evaluation infrastructure for generative AI models on large-scale GPU clusters. It involves developing sophisticated tools and techniques for reliability, performance, and health monitoring, and collaborating with model scientists on evaluation methods and inference strategies. The role also touches on pretraining software development and benchmarking.

Research intern role focused on developing new LLM memory representation and orchestration systems, including advanced RAG and embedding techniques, for enterprise and consumer applications.

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.

Senior Researcher in Microsoft Research AI Frontiers lab focusing on expanding AI capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms. The role involves research into Generative AI and Multimodal Model (MLM) technologies, including reasoning, architectures, training methods, action models, orchestration, multi-agent systems, and evaluation.

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.

This role focuses on building and optimizing the software stack for massive GPU clusters, high-throughput storage systems, and cutting-edge AI research. You will work closely with model scientists to scale up the latest research recipes, implement new forms of distributed training parallelism, and ensure the reliability and performance of thousands of GPUs across our supercomputing fleet. Profiling, benchmarking, debugging, and fine-grained optimization are core to this role, demanding both engineering rigor and creativity.

Senior Researcher in Microsoft Research's AI Interaction and Learning team, focusing on collaborative AI systems where humans and AI agents work together. The role involves pushing the boundaries of learning and reasoning with foundation models, developing innovative AI/ML techniques, and shaping the team's research agenda. Key interests include designing metrics for evolving goals, eliciting preferences under uncertainty, and managing information flow for coordination. The role requires a PhD and experience in areas like Foundation Models, RL, NLP, and Deep Learning, with a strong publication record in top-tier venues.

Senior Researcher in Generative AI at Microsoft Research AI Frontiers, focusing on expanding AI capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms. The role involves leading research in Generative AI and LLMs, with projects including Small Language Models and agentic AI systems. Key research areas include reasoning, action models for tasks, and evaluation of model/agent capabilities. The position offers a vibrant research environment with an open publication policy and opportunities for real-world impact.

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.

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.

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.

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.

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.

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.

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.

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.

Research Intern position in Machine Learning and Statistics at Microsoft Research New England, focusing on advancing the state-of-the-art in applied and theoretical ML research. Projects cover a broad range of ML topics including generative models, transfer learning, optimal transport, reinforcement learning, and deep learning.

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.

Research Intern position at Microsoft focusing on advancing artificial intelligence through research in areas like multimodality, world models, LLMs, computer vision, AI4Code, robotics, and agents. The role involves conducting research, developing novel methodologies, and disseminating findings through publications.

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.

Research intern role focused on advancing next-gen AI systems where humans and AI agents collaborate. The role involves research in areas like foundation models, reinforcement learning, multi-objective optimization, NLP, deep learning, interactive learning, agents, causal reasoning, computational social science, and human-AI collaboration, with a focus on shifting AI systems development towards optimizing complex objectives like group-level outcomes and team coordination. The internship is 12 weeks and involves collaboration with researchers and presenting findings.

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.

Research Intern position at Microsoft Research AI Frontiers lab focusing on expanding AI capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms. The role involves developing, improving, and exploring LLMs and Multimodal AI models, with research areas including reasoning, new architectures, action models, multi-agent systems, and evaluation.

Senior Software Engineer role focused on building LLM-powered data engineering experiences and infrastructure for Microsoft Fabric, utilizing Apache Spark. The role involves implementing agentic workflows, scalable LLM-backed data features, and defining evaluation strategies for LLM features, while applying Responsible AI practices.

Principal Software Engineer on the Azure Artificial Intelligence Core team, responsible for designing, building, and maintaining AI systems powering large-scale multimodal workloads. This role involves end-to-end ownership from API interfaces to inference backends, with a focus on safety, real-time audio interaction, image/video generation, and benchmarking/optimizing LLM performance. Collaboration with OpenAI and internal teams is key.

Software Engineer 2 on the Microsoft Azure AI Inference platform team, responsible for the hosting, optimization, and scaling of the inference stack for Azure AI Foundary models, including those from OpenAI and other OSS providers. The role focuses on designing and implementing core inference infrastructure, improving performance and efficiency for LLMs and GenAI models, and scaling the platform to meet growing demand.

Senior Applied Scientist to design and implement state-of-the-art ML models for Microsoft Ads and Copilot, impacting millions of users. Responsibilities cover the full modeling lifecycle from data strategy to deployment and evaluation, focusing on scalable solutions for ad relevance and user experience.

Principal Product Manager to define and drive the strategy for Vertical Knowledge Agents that combine structured data, licensed content, and AI grounding to deliver insights across Copilot, MSN, and Bing. The role involves influencing agent architecture, ranking frameworks, and multimodal UX, partnering with AI research and engineering to ship agentic experiences, and championing Responsible AI.

Senior Software Engineer on the Bing GenAI team, responsible for the architecture and development of high-performance services powering generative AI features in Bing and Copilot. This includes prompt engineering, C#-based service layers, and guiding project delivery for features like web grounding, search mode, answer cards, and generative UI. The role involves technical leadership, mentoring, and ensuring the availability, reliability, and performance of large-scale AI systems.

Senior Applied Scientist to lead the development of machine learning and generative AI systems for conversational commerce experiences within Microsoft Copilot. This role focuses on product discovery, ranking, personalization, and reasoning, involving LLM-based systems, RAG, tool orchestration, and addressing quality/trust challenges. The role also involves defining evaluation frameworks and partnering with product/engineering teams to deliver low-latency experiences.

Applied Scientist II on the Core Search and AI team (Bing) to build the next generation of search using large language models at scale. Responsibilities include developing retrieval, ranking, and relevance systems, refining RAG pipelines, and translating research into production deployments. Requires experience with ML systems for search/ads/recommendation and LLMs.

Product Manager for an Agentic Platform within Microsoft's Engineering Systems (ES365) team. This role focuses on building and scaling the agentic platform, including orchestration, developer tools, and governance, for use by thousands of Microsoft developers. The PM will define product vision, strategy, and metrics, work closely with engineering, and leverage AI tools in their daily work to improve developer efficiency and enable AI-assisted workflows across large codebases.

Principal Software Engineer at Microsoft CoreAI, focusing on building SDKs, UI, and agentic experiences for generative AI applications. The role involves defining, architecting, and developing agent platform services and developer experiences, enabling customers to build, deploy, evaluate, and manage intelligent agents at scale. Key responsibilities include leading API, SDK, CLI, and UI development, and owning architecture decisions for agent orchestration, knowledge integration, tool calling, and multi-turn conversations.

Manager for a Core Recommendation and Content Generation team at Microsoft, focusing on LLMs and NLP to drive user engagement across various Microsoft platforms. The role involves leading a team of Applied Scientists and ML Engineers, defining technical strategy, and overseeing end-to-end execution of ML solutions.

Senior Software Engineer role focused on building and evolving large-scale, cloud-native systems for the end-to-end lifecycle of intelligent agents. This includes secure enterprise deployment, governed tool integration, model fine-tuning, training workflows, and production observability, evaluation, and optimization.

Principal Applied Scientist role focused on building and scaling AI-powered content recommendation systems for Microsoft Copilot Discover. The role involves advancing the recommendation and ranking stack using LLMs and multimodal models, deepening user and content understanding, scaling ML/AI systems, and driving innovation in AI-forward products like agentic content feeds and generative UI. The position requires technical leadership, mentoring, and close collaboration with engineering, product, and policy teams.

Principal Data and Applied Scientist role within Microsoft's EngOps team, focusing on developing and deploying cutting-edge GenAI and ML solutions to improve Azure customer experience, security, reliability, and quality. The role involves the full AI project lifecycle, from ideation to production, with a strong emphasis on experimentation, evaluation, and collaboration with cross-functional teams. Requires experience in building and deploying live production systems and delivering AI product features.

This role is for a Principal Technical Program Manager (TPM) focused on building runtime defenses for agentic AI systems like Copilot. The TPM will own the end-to-end delivery of security capabilities such as misuse detection, adaptive guardrails, and containment mechanisms. The role requires translating ambiguous threat models into shippable, operable defenses in a scaled AI product, operating at the intersection of security engineering, AI research, and platform systems. It emphasizes direct technical execution and making complex systems land in production under adversarial pressure, rather than process or coordination.

Principal Software Engineer focused on optimizing the performance of AI model inference, particularly LLMs, across various hardware platforms (GPUs, Microsoft silicon). The role involves deep technical work on the AI software stack, from fundamental abstractions to system-level optimizations, aiming to improve efficiency and reduce costs for large-scale AI deployments, including those for Azure OpenAI service.

Senior Software Engineer to develop security features and solutions for agentic AI in Copilot, focusing on protecting customers and enabling new capabilities. The role involves designing and building AI-powered defenses, secure orchestration frameworks, and enabling technologies for safe and responsible AI actions at scale.

Principal Software Engineer at Microsoft Foundry, Core AI, focused on building and scaling the platform for intelligent agents and generative AI systems. This role involves driving technical direction, architectural decisions, and ensuring quality, reliability, security, and compliance for large-scale AI systems. The engineer will mentor others, influence without authority, and champion automation and responsible AI practices.

Senior Applied Scientist role focused on building and optimizing generative AI models, particularly LLMs and agentic systems, for Microsoft's consumer products like Bing and Copilot. The role involves hands-on work with SOTA models, prompt engineering, large-scale data analysis, and experimental design to improve search experiences and deliver impactful AI features.

Senior Software Engineer role focused on building the foundational platform and distributed systems for agentic AI applications at scale. This includes developing cloud-native infrastructure, runtime systems, and orchestrating agent-based AI applications with enterprise-grade guarantees, while integrating multi-modal capabilities.

Principal Applied Scientist Architect for the Core Recommendation Ranking team, focusing on integrating GenAI and agentic systems into large-scale content recommendation and ranking stacks for consumer-facing Microsoft surfaces. The role involves designing, implementing, and architecting advanced ML/DL models, including LLMs, for ranking, reranking, and retrieval, with a strong emphasis on production ML systems at scale and cross-team technical leadership.

This Principal Product Manager role within Microsoft AI focuses on defining the vision, strategy, and execution for AI training pipelines. The role involves leading cross-functional teams to build scalable and high-quality training data collection and processing systems, defining success metrics, and ensuring compliance. It requires deep collaboration with engineering, research, and data science to operationalize new evaluation techniques and metrics for LLM and ML-based products, with a strong emphasis on data processing, infrastructure, and evaluation systems.

Software Engineer role focused on building and managing large-scale GPU infrastructure and training/inference platforms for AI models (LLMs, SLMs, multimodal, code-specific) on Azure and partner clouds. Responsibilities include architecting, designing, and developing core AI infrastructure services, collaborating with researchers, and enhancing system stability, latency, security, and maintainability for training runs.