Principal Software Engineer on the AI Frameworks team at Microsoft, responsible for developing and evaluating core algorithmic and hardware technologies for large-scale AI model training and inference on novel hardware. Collaborates with ML researchers, system engineers, and partners to optimize and scale AI models, build validation tools, and perform software development in languages like Python, C/C++, and CUDA.

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.

Senior Applied Scientist role focused on building and scaling an AI-driven testing platform for Microsoft's AI products. The role involves using LLMs, prompt engineering, and agent-based workflows to validate product quality, design testing strategies, develop evaluation frameworks, and create actionable insights. The goal is to evolve the platform towards more autonomous and agentic workflows.

Senior Software Engineer focused on building and scaling AI/ML-powered features for Windows, integrating multimodal input, agentic workflows, and generative AI. The role involves end-to-end development, from research and implementation of foundation models, prompt engineering, RAG, and multi-agent architectures, to fine-tuning, evaluation, and production deployment. Emphasis on creating inclusive and accessible experiences, particularly for users with disabilities, using human-centered AI.

The Senior AI Product Manager will drive Copilot model capabilities such as tool-use to ensure that the language models that power Microsoft Copilot deliver high quality responses to our users whilst being grounded, reliable, and cost-efficient. This role works at the nexus of product and research, driving execution in partnership with engineers, language engineers, data scientists and researchers. Responsibilities include developing LLM platform strategy, prototyping approaches, identifying and prioritizing issues impacting quality/factuality/safety, defining and building evaluations, defining and deploying experiments for tool use, and partnering with teams to scale tool building and resolve dependencies.

Product Manager for Microsoft's Agentic Engineering platform, focusing on building and scaling AI-powered developer tools and experiences for thousands of Microsoft engineers. The role involves defining product vision, strategy, and roadmap for the core orchestration layer, developer tools, plugin marketplace, and enterprise governance, with a strong emphasis on hands-on use of AI tools and driving adoption.

AI Security Research Intern focused on developing autonomous systems to detect and disrupt cyber attacks in near real-time, leveraging LLMs and agentic frameworks.

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.

Applied AI Engineer role focused on building and shipping LLM-powered assistant features, agentic behaviors, and retrieval pipelines. Responsibilities include prompt architecture, orchestration logic, evaluation frameworks, hillclimbing for performance improvement, and developing internal tooling for experimentation and debugging. The role also involves integrating LLMs with product surfaces and building lightweight ML components for enhanced assistant intelligence. Requires experience in production-level code/models and working in fast-moving AI teams with a startup-like energy.

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.

Senior Software Engineer focused on building Responsible AI services, including identifying, measuring, mitigating, and monitoring AI risks across various content types. The role involves designing and developing large-scale distributed cloud services with a focus on safety, governance, inference, evaluation, and multimodal safety infrastructure.

Engineering Manager to lead a team focused on building and scaling an agentic engineering platform for AI-powered developer productivity across Microsoft. The role involves managing engineers, setting team direction, fostering experimentation, and partnering with other teams to advance AI in developer workflows.

The Principal Software Engineer will build and own the agentic engineering platform used across Microsoft, orchestrating AI models to enhance developer productivity. This role involves designing, implementing, and shipping features for the platform, advocating for best practices in agentic development, and mentoring other engineers. The focus is on creating the core agentic platform and experiences, with a tight feedback loop from internal developer customers.

Senior Software Engineer to build and own the agentic AI platform for developer productivity at Microsoft scale, orchestrating AI models and experiences for thousands of developers.

Senior Researcher focused on advancing efficiency across the AI stack for generative AI serving systems, spanning models, ML frameworks, cloud infrastructure, and hardware. The role involves algorithmic and systems optimization for latency, throughput, and cost, with a strong emphasis on driving research ideas through prototyping, validation, and production deployment.

Research intern position focused on computer vision and deep learning to improve user understanding of devices and environments. Involves analyzing multimodal sensor data and human-object interactions to enhance human-computer interactions, with opportunities to advance the state of the art.

Principal Product Manager for Microsoft Edge, focusing on defining and executing an AI-native product strategy to enhance user experience and collapse the distance between user intent and accomplishment. The role involves inventing new AI-driven features, translating frontier AI capabilities into customer-facing products, and driving product strategy from research to shipped product.

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.

Senior Research Software Engineer on the Agentic Experiences team at Microsoft Research. The role involves designing and building software using AI tools and agentic workflows across the SDLC, from prototypes to scalable products. Responsibilities include coding, design, engineering excellence, cross-project collaboration, and technical leadership, with a focus on AI-native development and agentic experiences.

Principal AI Software Architect role focused on enabling and optimizing machine learning model training workflows on custom hardware (MAIA accelerators). Requires expertise in PyTorch, Triton/CUDA, and understanding of accelerator architecture for efficient deployment of large models.

Principal Software Engineer focused on optimizing GPU inference for large-scale deep learning models (LLMs/SLMs) within Microsoft's AI-native monetization platform, serving ads, shopping, and Copilot.

Product Manager for Microsoft Foundry's AI Models & Training organization, focusing on platforms and infrastructure for training, evaluating, launching, and operating AI models at scale. The role involves defining product strategy and roadmaps for model training, inference, experimentation, and platform enablement, driving initiatives across the AI model lifecycle, and enabling internal teams and customers to access and adopt models. Requires strong technical fluency, ability to lead cross-functional initiatives, and experience with AI/ML platforms and infrastructure.

Principal Research Software Engineer to provide technical leadership and direct technical contribution on the AI Agentic Core Team. The mission is to accelerate the path from research to product by building AI-driven systems, workflows, and platforms that help researchers and product teams move faster from exploration to real-world impact. This role involves collaborating with engineers, researchers, and product teams to build high-impact systems spanning early-stage prototypes through production-ready tools, services, and experiences, while modernizing how software is designed, built, evaluated, and shipped. The role requires designing, developing, and shipping systems that transition MSR concepts into production-quality tools, services, and product capabilities, owning the end-to-end engineering lifecycle. It also involves defining and implementing AI-driven processes that accelerate research-to-product pipelines using LLMs, agentic workflows, and modern developer tooling, including designing and integrating agentic AI frameworks and LLM-based pipelines, developing tool-use and function-calling architectures, and applying prompt design, RAG, and evaluation frameworks. Contributions to model experimentation and fine-tuning are also part of the role.

Principal Engineer on the AI Core Infrastructure team, responsible for large-scale GPU management infrastructure and inference/training platforms powering Microsoft's AI workloads. The role involves setting roadmaps, designing backend services, and providing insights for customers to monitor, troubleshoot, and scale AI training workloads on supercomputers. Focus on ML infrastructure, distributed systems, and observability.

Principal Software Engineer on the AI Frameworks team at Microsoft, focusing on developing and optimizing software for running AI models across diverse hardware platforms. This includes working on ONNX, ONNX Runtime for high-performance inferencing and training acceleration, and Foundry Local for on-device inference.

Principal Software Engineer role focused on building and supporting large-scale GPU management infrastructure and inference/training platforms for AI workloads at Microsoft. The role involves architecting, designing, and developing core AI infrastructure services and compute, storage, and networking subsystems for LLM training, customization, and inference.

Senior Software Engineer focused on building and optimizing foundational inference engines and APIs for large-scale AI inference across Azure. The role involves improving latency, throughput, availability, and cost for LLMs, working with OpenAI and open-source models, and developing experimentation capabilities for safe and rapid iteration.

Principal Software Engineer focused on building and optimizing foundational inference engines and APIs for large-scale AI inference across Azure. The role involves driving production-grade serving improvements for OpenAI and open-source LLMs, focusing on latency, throughput, availability, and cost efficiency. Responsibilities include making hands-on engine changes, building experimentation capabilities, and designing inference serving architectures to support multitenant AI systems at global scale.

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.

The AI Platform Engineer role focuses on building and improving systems within the response quality stack for AI products at Microsoft. This includes data pipelines, evaluation frameworks, RAG, and inference serving. The role involves designing and operating a data flywheel to collect production signals for model improvements, identifying and fixing quality degradation, shipping production retrieval systems, and collaborating with researchers and cross-functional teams to translate quality insights into shipped improvements. The position requires production code ownership end-to-end and emphasizes rapid iteration and deployment.

Senior Applied AI Engineer role focused on building and shipping LLM-powered assistant features and agentic systems. Responsibilities include designing and developing conversational flows, retrieval pipelines, and multimodal interactions using prompt architectures and orchestration logic. The role also involves building evaluation frameworks, running hillclimbing loops for continuous improvement, and developing internal tools for experimentation and debugging. Integration with product surfaces and building lightweight ML components are key. The team operates with startup energy in a fast-moving AI environment.

Principal Software Engineer role focused on building and scaling the AI data-plane for LLM inferencing across Microsoft and Azure. The role involves designing, coding, and shipping core serving systems, smart routing, and request distribution for a wide range of LLMs, aiming for reliability, efficiency, and ultra-low latency.

This role focuses on building and optimizing high-performance runtime systems for large-scale LLM inferencing, specifically for OpenAI chat and multimodal AI models. The engineer will be responsible for systems-level optimization, microservice design, and ensuring the latency, throughput, cost, and reliability of AI inference pipelines.

This role is for a Principal Applied Science Manager at Microsoft Bing's RAI Defensives team. The primary focus is on managing a team to research and ship state-of-the-art Deep Learning (DL) based NLP models for web-scale services, specifically for detecting harmful queries and content to ensure customer safety and a positive user experience. The role involves people management, technical leadership, and delivering reliable, scalable distributed services.

Senior Applied AI Engineer focused on building and shipping image generation capabilities for AI assistants and productivity tools. This role involves model development, training, fine-tuning, evaluation, and production deployment, with a strong emphasis on integrating LLMs and delivering customer-facing features.

This role focuses on building and optimizing AI Agents with world-class orchestration and inference layers. The engineer will develop secure and performant AI Platform services that power products like Copilot, working collaboratively with researchers and other engineers to create next-generation AI products and services. The emphasis is on shipping high-quality, well-tested, secure, and maintainable code within a fast-paced development cycle.

Applied Engineer II role focused on developing and integrating AI technologies, including foundation models, prompt engineering, RAG, and multi-agent architectures. The role involves fine-tuning models, evaluating behavior, building prototypes, and deploying solutions into production, with a strong emphasis on responsible AI and ethical considerations.

Principal Technical Program Manager for the OneDrive and SharePoint Applied Science team, focusing on driving high-ambition applied research in areas like agentic AI, retrieval, and content understanding to reshape knowledge management within Microsoft 365. The role involves shaping strategy, managing complex science-heavy initiatives, and ensuring scientific advancements translate into product and platform impact, with a focus on enabling invention at scale and influencing organizational direction.

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.

This role focuses on prompt engineering for an AI companion, aiming to create empathetic and collaborative user experiences. Responsibilities include designing prompting techniques, establishing frameworks for social and ethical navigation, and creating evaluations for AI behavior. The role requires experience with chain-of-thought prompting, scaled implementation of prompted systems, and evaluation design.

The role focuses on designing and optimizing AI Agents and their orchestration layers for Copilot and other AI-powered experiences. It involves building robust systems for multi-model/multi-service workflows, pushing inference performance for low-latency execution, and enabling agents to securely and efficiently call APIs and services. The ideal candidate has backend or ML systems expertise at the intersection of product and inference, building scalable and reliable AI platforms.

Senior Applied Scientist with expertise in NLP, deep learning, and Ads recommender systems to design and implement ML models for relevance systems across Microsoft Ads and Shopping (Bing, Copilot). The role focuses on enhancing ad relevance and optimizing user experiences, operating end-to-end for ads and shopping products.

Principal Product Manager for Search & AI team, focusing on next-generation agentic AI products. The role involves defining vision and execution for generative experiences across search, chat, and other agentic applications, empowering users to accomplish complex tasks. Responsibilities include roadmap definition, turning model capabilities into product features, working with cross-functional teams, and using evaluation methods for product quality and safety. The role emphasizes driving DAU growth through product strategy, distribution tactics, and partnerships.

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.

Senior Applied AI Engineer for the Customer Service Applications Team, focusing on developing and integrating AI technologies, including foundation models, prompt engineering, RAG, graphs, and multi-agent architectures, into Microsoft products. The role involves fine-tuning models, evaluating their behavior, building prototypes, contributing to production deployment, and translating research into scalable, impactful solutions. Emphasis on responsible AI practices and leveraging AI for real-world customer service outcomes.

This role focuses on building and scaling the high-performance computing (HPC) infrastructure required for training frontier AI models and powering AI products like Copilot. The engineer will design, operate, and maintain large-scale HPC environments, including schedulers, GPU compute, storage, and networking. Responsibilities include developing automation, supporting researchers and engineers, and troubleshooting cluster issues. The role requires experience with on-premise or cloud HPC clusters, high-scale training clusters, and public cloud infrastructure.

Research intern to study LLM-based assistants' behavior with incomplete information, focusing on uncertainty awareness, responsible reasoning, and robustness for enterprise AI systems.

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.