This role focuses on the applied science foundation for observability in AI agents and multi-agent systems running at scale. It involves developing and applying scientific methods, evaluation frameworks, and measurement systems to understand, benchmark, diagnose, and improve agent-based systems in production. The role addresses unique observability challenges of AI agents, such as non-deterministic execution and emergent behaviors.

This role focuses on optimizing and managing the compute infrastructure for training large-scale AI models. The responsibilities include designing and implementing distributed training systems, building telemetry for performance monitoring, profiling and debugging bottlenecks, and driving architectural improvements for efficiency. The role requires strong software engineering skills in Python and C++, deep understanding of GPU architectures, and experience with distributed computing systems and ML workloads.

This role focuses on building and evolving the core orchestration and intelligent systems that power Microsoft's enterprise Copilot, a large-scale AI assistant for Microsoft 365. The work involves advancing model training, developing model-driven features from concept to production, and enhancing the orchestrator for improved reasoning, speed, and multi-modal/agentic capabilities. The goal is to ship cutting-edge AI technology impacting hundreds of millions of users.

Senior Software Engineer on the FIT training team within Microsoft's CoreAI organization, focused on building and optimizing AI infrastructure for agentic AI systems. The role involves developing scalable infrastructure for training LLMs, SLMs, and agentic models to achieve frontier-level performance, contributing to both proprietary and open-source frameworks for enterprise-grade agentic workflows.

This role focuses on building and optimizing the AI infrastructure for training agentic AI systems, including LLMs and SLMs, to achieve frontier-level performance. It involves developing scalable infrastructure and services for training, deploying, and monitoring these models in a cloud environment.

Principal Tech Lead Manager for Copilot's image generation capabilities, focusing on technical leadership, product delivery, evaluation systems, and team building. This role involves defining the technical roadmap, integrating new models, scaling evaluation frameworks, and leading a team of AI engineers to improve image quality and user satisfaction in a high-impact consumer product.

The AI Core Infrastructure team is responsible for building and managing the large-scale GPU management infrastructure and inference/training platforms that power Microsoft's AI workloads. This role focuses on the training infrastructure for large-scale model pre-training, post-training, and fine-tuning on advanced GPUs in Azure and partner clouds.

This role focuses on building and maintaining large-scale infrastructure for multimodal generative models, covering the full development cycle from data processing to training, inference, and serving. It involves working with research scientists and product engineers to optimize performance and drive architectural changes for consumer AI products like Copilot.

This role focuses on the co-design and productionization of next-generation AI systems at datacenter scale, optimizing end-to-end performance and efficiency. It operates at the intersection of models, systems software, networking, storage, and AI hardware, influencing accelerator design, system architectures, and large-scale AI platforms. The role involves analyzing real workloads, developing performance models, and partnering with various teams to drive high-impact ideas into production systems. It also contributes to research and the broader community through publications and open-sourcing.

Seeking engineers and researchers to join the Pretraining Text Data team to build the next generation of foundation large language models. The role involves designing and curating high-quality text datasets, developing novel data collection strategies, improving dataset quality, understanding data-driven model behaviors, and aligning datasets with ethical values. Responsibilities include creating datasets for training and evaluation, developing scalable data pipelines, analyzing datasets, building tools for auditing, and collaborating with safety and ethics teams.

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.

This role focuses on designing, developing, and operating large-scale reinforcement learning systems for training agents and reasoning models. It involves contributing to cutting-edge research and bridging the gap between research and production-grade distributed systems, with responsibilities including tuning pretraining software for specific GPU architectures and contributing to AI model development.

Technical Program Manager for Microsoft AI's Superintelligence Team, focused on training frontier AI models. The role involves end-to-end program planning, coordinating with researchers and engineers, managing timelines, mitigating risks, and ensuring the responsible advancement of AI.

This role is focused on building and advancing large-scale foundation models, with a specific emphasis on multimodal capabilities and ensuring AI systems are controllable, safety-aligned, and anchored to human values. The position involves algorithm development, model architecture design, experimentation, data pipeline innovation, and improving training/deployment efficiency, aiming to push the frontier of AI responsibly.

This role is focused on training frontier AI foundation models at Microsoft AI, specifically within the Pre-Training team of the Superintelligence Team. The responsibilities include developing algorithms, model architectures, data mixtures, and scaling laws for large-scale training, driving implementations, conducting experiments, and overseeing training runs. The role emphasizes collaboration with infrastructure, data, post-training, and multimodality teams.

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.

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.

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 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.

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.

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.

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.

This role focuses on building and productionizing machine learning and generative AI systems for conversational commerce experiences within Microsoft Copilot. It involves developing models for product discovery, ranking, personalization, and reasoning, as well as LLM-based systems for conversational shopping, including RAG and tool orchestration. The role also addresses quality and trust challenges and defines evaluation frameworks, aiming to translate models into low-latency, reliable user-facing experiences.

The AI Frameworks team at Microsoft develops software for training and deploying advanced AI models, collaborating with hardware teams and partners on supercomputers and AI accelerators. This role involves developing and evaluating core algorithmic and hardware technologies for large-scale AI model training and inference, working closely with ML researchers and developers, and with OpenAI on Azure OpenAI service models. The position requires hands-on software design and development skills in languages like Python, C/C++, and CUDA, focusing on LLM optimization technologies, model scripting, and kernel languages.

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.

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.

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.

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.