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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Research Intern position focused on applying machine learning techniques to biology and healthcare problems, including generative models, computational immunology, and statistical genetics.

This role focuses on advancing the state-of-the-art in AI/ML research and driving innovation from theory into reality by blending research and development techniques. The individual will work on the design, development, execution, and implementation of technology research projects, with a focus on technology transfer into products and services. The role requires a PhD student in a related field and involves understanding research literature, preparing data for analysis, and contributing to patents and white papers.

Research intern position focusing on generative audio, AI for audio, speech enhancement, spatial audio, and audio devices. The role involves working on well-defined projects, collaborating with researchers, and potentially publishing findings. Requires enrollment in a PhD program and experience in audio signal processing. AI/ML experience is strongly preferred.

Research intern to conduct research and lead collaborations on incorporating AI capabilities into Azure SQL Database platform. Focus on advancing state-of-the-art in machine intelligence and machine learning applications, implementing prototypes, and developing solutions for large-scale problems. Experience with LLMs and ML techniques is preferred.

Senior Researcher at Microsoft Research focusing on AI and Systems Reliability. The role involves defining a novel research agenda in areas like distributed systems, formal methods, ML for system reliability, and reliability of ML systems, with the goal of bringing formal rigor and reliability guarantees to AI-powered platforms.

Research Intern position at Microsoft Research's Inclusive Futures group, focusing on needs-focused research for underserved individuals. Requires a PhD student in a relevant STEM field with experience in HCI, AI, Accessibility, or related areas. The role involves conducting original research, collaborating with researchers, and potentially publishing findings.

Research Intern position focusing on adapting and designing AI systems for religious communities, involving model evaluations, product design, and responsible AI practices. The role requires a PhD student in a STEM field with an interest in religion and experience with emerging technologies like Generative AI and Agentic Systems.

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.