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.

The role focuses on integrating custom AI silicon with AI inference frameworks like SGLang, optimizing LLM inference performance, and developing custom operators. It involves working with hardware accelerators and potentially non-CUDA ecosystems, aiming to improve AI workload efficiency.

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.

Senior Research Scientist to lead cutting-edge projects from concept to product, focusing on AI for Developers. This role involves building and training state-of-the-art models, applying and advancing techniques for leveraging LLMs in software engineering (including RAG and evaluation), and collaborating with product teams to run large-scale experiments and improve AI solutions. The primary focus is on AI for code completion and editing, retrieval-augmented systems for codebases, and efficient inference algorithms for code generation.

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 role focused on agentic programming, involving building agentic workflows and ML models for software engineering tasks, within a PhD program context.

This role focuses on building and scaling large-scale AI systems for Copilot memory and personalization, leveraging technologies like RAG, embeddings, retrieval, and ranking. The engineer will be responsible for designing and implementing these systems, collaborating across product groups, and ensuring system reliability and performance.

Senior Researcher at Microsoft Research focusing on the intersection of AI systems and society, with an emphasis on sociotechnical approaches to AI evaluation, responsible AI in industry, and AI safety. The role involves interdisciplinary research, collaboration with industry teams, and a strong publication record.

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.

Applied Research role focused on hardware/software codesign for GPU kernel optimizations to improve efficiency of Large Language Models and Generative AI inference. Involves designing, implementing, and optimizing GPU kernels, researching novel optimization techniques, and profiling performance.

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

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.

Principal Software Engineer to architect and implement an agentic auto-bidding platform for digital advertising, leveraging AI, machine learning, and large-scale distributed systems to optimize bids in real-time across Microsoft's marketplaces. The role involves defining requirements for AI-driven capabilities, building data validation frameworks, ensuring operational excellence, and exploring emerging AI techniques like multi-agent systems.

Principal Applied Scientist role focused on the future of AI for Developers, leading research projects from inception to product integration. The role involves building and training state-of-the-art models, applying LLMs to software engineering tasks (including RAG and evaluation), and collaborating with product teams to scale and improve AI projects. The role also involves creating new datasets and managing large-scale ML experiments.

Research Intern role focused on advancing the quality, reliability, and evaluation of LLM-based systems by exploring new ML methods for AI assessment and alignment. Responsibilities include co-developing research projects, implementing ML approaches (training/fine-tuning), and developing evaluation frameworks. Requires PhD enrollment in a technical field and hands-on LLM experience.

Principal Applied Scientist role focused on building and shipping AI agentic applications and predictive models for Viva Engage. The role involves end-to-end delivery of ML capabilities, including problem framing, data handling, modeling, evaluation, experimentation, deployment, and monitoring. Emphasis is on production-viable solutions, leveraging LLMs and generative AI for knowledge discovery, moderation, recommendations, and enhancing user experiences. The position requires hands-on coding in production environments and driving measurable product impact.

Senior AI Software Architect role focused on optimizing AI model performance and enablement on Maia hardware, involving PyTorch, quantization, parallelization, and inference pipelines.

Research intern to conduct threat modeling for LLM-enabled agentic designs, develop formal security frameworks, and produce security proofs and empirical validations. Deliverable within 12 weeks.

The Principal Software Engineer will work on an auto-bidding platform for digital advertising, leveraging AI, machine learning, and large-scale distributed systems. The role involves designing and building an agentic bidding platform that processes billions of auction events daily, optimizing bids in milliseconds. Responsibilities include defining requirements for agentic AI-driven bidding capabilities, architecting the next-generation platform, building data validation frameworks, driving operational excellence, developing experimentation frameworks, adopting new technologies, mentoring engineers, and exploring emerging AI techniques like multi-agent systems.

This role focuses on designing, scaling, and optimizing high-performance networks for AI training and inference clusters. The engineer will work on the end-to-end networking architecture, from link-layer to fabric-wide systems, connecting thousands of GPUs. Responsibilities include benchmarking, profiling, debugging, and tuning AI workloads, engineering ultra-low-latency networks, and designing congestion-free transport mechanisms. The goal is to build networking systems that directly accelerate Microsoft's frontier AI models and support the development of advanced AI systems.

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.

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.

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.

This role focuses on designing and developing LLMs and underlying subsystems for enterprise AI products, specifically within Microsoft 365 Copilot. It involves tailoring models to product scenarios, collaborating with research and engineering teams, and building custom LLMs and architectures for GenAI applications. The role requires a strong understanding of AI/ML product cycles and the ability to deliver solutions from ideation to shipping.

Applied Scientist role focused on building advanced Copilot features like Deep Research and Web artifact generation. Responsibilities include architecting and implementing LLM-powered systems, leading evaluation efforts, designing data pipelines for prompt engineering and fine-tuning, and training content classifiers. Requires experience with LLMs, production-quality Python code, and a Bachelor's degree with related experience.

Research intern role at Microsoft Teams focusing on AI-driven innovation in collaboration and communication technologies. Projects may involve AI agents, conversation intelligence, meeting efficiency, and user experience, with a strong emphasis on privacy, security, and accessibility. The role involves applying research methods to real-world challenges in digital collaboration.

This role focuses on building and optimizing the compute orchestration and scheduling layer for large-scale AI model pretraining, utilizing Kubernetes and Ray. It involves workload placement, scaling, reliability, and developer experience, with a direct impact on AI model development and deployment infrastructure.

Seeking Data Research Engineers to join the Multimodal team, focusing on designing and curating high-quality datasets for next-generation foundation models across vision, language, and audio. The role involves developing data collection strategies, improving dataset quality, analyzing data-driven model behaviors, and building tools for dataset auditing, all within the context of responsible AI practices.

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.

This role focuses on building and optimizing the infrastructure and developer experience for large-scale ML model training and inference, specifically for Microsoft's AI assistant, Copilot. The responsibilities include improving CI/CD pipelines, developing training tools, enhancing cloud infrastructure, and managing model hosting systems for inference and data generation. The role aims to accelerate iteration and improve the quality of AI models powering innovative products.