Microsoft

Principal Software Engineer to build and operate end-to-end multimodal generative AI systems for Azure Content Understanding, focusing on extracting insights from diverse data types. The role involves technical leadership, system design, quality improvement through evaluation, and collaboration with security and product teams.

This role focuses on establishing and operating an AI Innovation Lab within Microsoft's Customer Experience and Success organization. The primary responsibility is to research, validate, and evaluate early-stage AI opportunities (pre 0-to-1) before they are handed off for prototyping or product engagement. This involves defining research frameworks, building evaluation approaches for various AI workloads, developing automated testing, and creating reference architectures. The role acts as a bridge between business strategy, research, and technical execution, ensuring AI innovations are rigorously tested and aligned with Responsible AI standards.

Principal Software Engineer focused on building AI-powered security systems for Microsoft's cloud environment. The role involves designing, building, and operating production AI services that combine LLMs, agentic workflows, RAG, knowledge graphs, and multi-modal processing, with a strong emphasis on evaluation, responsible AI, and scalability within a large cloud platform. The candidate will lead architecture, design, and delivery of these systems, collaborating across various teams to translate AI advances into practical security solutions.

AI Engineer role focused on designing, building, and deploying AI-native systems for enterprise customers to securely adopt AI at scale. This involves working across the AI lifecycle, including model development, data pipelines, deployment, monitoring, and MLOps, with a focus on LLM-based applications, RAG, and agentic workflows within a security context.

Senior Software Engineer to design and ship core components of an Agentic support platform, owning features end-to-end from prototype to production. Responsibilities include working across orchestration, grounding, evals, observability, and SDK surfaces, while ensuring production-readiness, trustworthiness, and scalability of AI-managed support systems.

Senior AI Hardware Architect role focused on defining and optimizing next-generation AI accelerator platforms and large-scale AI systems. Responsibilities include analytical performance modeling, workload characterization, profiling, and end-to-end performance analysis across GPU and accelerator architectures, working across hardware, software, and system boundaries. The role involves analyzing AI workloads, identifying performance bottlenecks, developing models for new architectural features, and correlating silicon data with models to drive optimizations for performance, efficiency, and TCO. Collaboration with various hardware and software teams is key to shaping future AI accelerator and system architectures.

Applied Scientist II at Microsoft AI focusing on Generative AI and Agentic Modeling for consumer products like Bing and Copilot. The role involves building and optimizing production ML models, working with SOTA generative models, analyzing large-scale data, designing experiments, and delivering insights for business decisions. Requires expertise in ML, Generative AI, Agentic Modeling, or Data Science, with hands-on experience with LLMs/SLMs.

This role focuses on applying and implementing novel AI technologies into production for Microsoft's products and services, acting as a bridge between research and development. It involves selecting and applying ML techniques to large datasets, staying current with research, preparing data, and communicating results. The program offers mentorship and exposure to leaders, with opportunities to join product teams post-program. A PhD is preferred for advanced proficiency in scientific methodology and research.

Principal Software Engineer to build AI capabilities that automate red team engagements using generative AI and agentic systems, advancing state-of-the-art attacker TTPs, and building online services for security defenders.

This internship focuses on applying advanced machine learning techniques to solve complex business challenges in areas like search, personalization, NLP, computer vision, and recommendation systems. The role involves developing and scaling models, preparing datasets, building ML pipelines, and collaborating with cross-functional teams to deliver product-integrated solutions, with a focus on influencing future AI experiences.

Principal Applied Scientist role focused on designing and developing LLMs and underlying subsystems for Microsoft 365 Copilot and Agents Core. The role involves tailoring models for product scenarios, working with research and engineering teams, and delivering joint-class solutions, including custom LLMs and architecture for specific product needs.

This role focuses on designing, training, and improving large-scale machine learning models for Bing Search relevance and ranking, leveraging LLMs for various understanding and summarization tasks. The goal is to deliver high-quality, low-latency search results at a global scale, involving end-to-end model development and optimization of multi-stage ranking stacks.

Senior Software Engineer to design and ship core components of an agentic sales platform, owning features end-to-end from prototype to production. Responsibilities include working across orchestration, grounding, evals, observability, and SDK surfaces, while raising the bar for agent development and evaluation. Requires experience building with agent stacks and a strong understanding of AI-native development principles.

Senior Applied Scientist focused on retrieval technologies for Microsoft 365 Copilot, specifically Search, Chat, and Agent experiences. The role involves designing, experimenting with, and evaluating retrieval and ranking systems, including semantic, dense, sparse, and hybrid retrieval, RAG, and LLM-integrated architectures, to enhance grounding, relevance, and reasoning for millions of enterprise users.

Microsoft Research Cambridge is seeking two researchers for a two-year postdoctoral program to advance the design and evaluation of next-generation datacentre networks specifically for AI training and inference workloads. The program involves two tracks: one focused on analytical modeling and simulation, and the other on systems implementation and experimental validation using advanced hardware testbeds. The goal is to publish research findings and influence future AI infrastructure strategy.

Senior Data Scientist role focused on end-to-end delivery of strategic data science and AI solutions for clients, including generative AI applications and agentic AI solutions. Responsibilities include business understanding, data preparation, modeling, insight communication, and collaboration, with a strong emphasis on responsible AI principles and integrating AI into client workflows.

Senior Applied Scientist role focused on bringing state-of-the-art AI/ML research into production for Microsoft products and services. This involves identifying trends, developing research-backed solutions, bridging the gap between research and development, and leveraging data analysis for modeling. The role requires expertise in ML subareas and applying them to solve complex business problems, ultimately impacting Microsoft products.

This role focuses on developing and industrializing deep learning speech technologies, with a specific emphasis on integrating speech with LLMs for multimodal modeling. The scientist will invent new algorithms, apply them to real products, and evaluate their performance against guardrails, impacting millions of users.

Postdoctoral Researcher focused on integrating experimental biological data with machine learning models for biomolecular simulation and drug discovery. The role involves designing and scaling experimental datasets, developing methods to connect ML models with experimental observables, and creating closed-loop workflows between models and experiments.

This role focuses on building and leading the development of advanced multilingual speech models, AOAI finetuning, and multimodal generative AI for Microsoft's Azure Speech team. The goal is to create transformative speech technologies for voice agents, transcription, and call centre analytics, impacting billions of users globally, with a special focus on India. The role involves setting technical direction, driving innovation, scaling model quality, and delivering breakthrough technologies.

Develops software for AI/ML frameworks and tools, focusing on ONNX and ONNX Runtime for high-performance inference and training acceleration across various hardware. Also works on on-device AI inference solutions.

This role focuses on designing, developing, and optimizing physics-based simulation frameworks for robotics applications. It involves creating high-fidelity simulation environments for robot kinematics, dynamics, sensors, and actuators, enabling reinforcement learning training, closed-loop policy evaluation, synthetic data generation, and sim-to-real transfer. The role bridges advanced physics simulation, robotics autonomy, and ML infrastructure to accelerate the development and deployment of physically grounded AI.

Applied Researcher role focused on post-training code specific models and agentic research for developer tools like Github Copilot and VS Code. The role involves building and managing large-scale ML experiments, creating new datasets, and collaborating with product teams to take research from concept to product.

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.

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 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.

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 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.

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.

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 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 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.

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 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.

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.

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.

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.

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.

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.