The Member of Technical Staff, Microsoft Robotics (Spatial AI) role focuses on designing, developing, and testing physical world models for robots to understand, predict, and reason about 3D physical environments. This involves building models for spatial structure, object relationships, physics dynamics, and scene semantics, enabling robots with physical intuition for manipulation, navigation, and interaction planning. The role contributes to world modeling, spatial AI, and foundation models for robotics, predicting physical world changes in response to robot actions. Responsibilities include designing and evaluating world models, building training data pipelines, and collaborating with researchers and engineers.

Develop, train, evaluate, and deploy machine learning models for robots to perceive, reason, and act in the physical world, focusing on vision-language-action (VLA) and similar models. This role involves the full robot learning stack, from data pipelines and model experimentation to large-scale training and real-world deployment on physical robots.

The Principal Applied Scientist will drive the applied science foundation for observability in AI agents and multi-agent systems at scale. This role focuses on understanding agent behavior in production, developing scientific methods, evaluation frameworks, and measurement systems to diagnose and improve agent-based systems. Responsibilities include developing evaluation and measurement frameworks, designing methodologies to connect offline evals with online signals, defining quality signals and benchmarks for agent systems, building models for behavior analysis, advancing observability through new approaches, partnering with engineering to operationalize methods, and contributing to instrumentation standards. The role also involves technical leadership, setting scientific direction, and collaborating with product and platform teams.

Principal Software Engineer role focused on building foundational platforms for intelligent agents and generative AI systems. Responsibilities include developing large-scale, cloud-native systems for agent deployment, execution, tool integration, fine-tuning, training, observability, evaluation, and optimization in production. The role operates at the intersection of distributed systems, AI infrastructure, and developer platforms, requiring strong systems thinking and architectural decision-making for systems demanding high performance, reliability, security, and compliance.

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 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.

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.

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 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 designing, shipping, and operating agentic support platforms and AI-driven autonomous support workflows for Microsoft's commercial business. The role involves end-to-end ownership of features, including orchestration, grounding, evals, observability, and SDK surfaces, with a strong emphasis on reliability, trustworthiness, and scalability of AI-managed support systems.

Principal Applied Scientist to lead the development of ML and generative AI systems for conversational commerce experiences within Microsoft Copilot. The role involves product discovery, ranking, personalization, reasoning, LLM-based systems, RAG, tool orchestration, and addressing quality/trust challenges, with a focus on shipping low-latency, reliable user-facing features. Technical leadership and Responsible AI practices are also key.

Software Engineer II role on the Azure AI Foundry Customization team, focused on building and scaling the AI platform for Azure and Microsoft's flagship products. The role involves developing pre-training, mid-training, and post-training solutions, working with LoRA models, and handling inference at scale. Responsibilities include creating abstractions, infrastructure, and features for training, testing, validation, scaling, and optimization of ML algorithms, as well as driving customer-inspired innovations and ensuring code quality and security.

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.

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 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 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.

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 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.