Microsoft has 521 active AI-related job listings. The majority of these roles are focused on agents, representing 37% of the total, followed by application and serving infrastructure. Engineering is the most frequent function, with a significant number of openings, and the United States is the primary hiring country. Frequent tech tags include agent orchestration, model serving, and LLM observability, suggesting a focus on operationalizing AI models. Over the last 30 days, Microsoft has added 280 new AI roles, a 157% increase compared to the previous 30-day period.
Currently tracking 250 active AI roles, down 24% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$331k (avg $195k).
Microsoft currently has 343 active AI-related roles in our index. The most common open titles are: Principal Software Engineer (19), Senior Software Engineer (19), Software Engineer II (8), Principal Applied Scientist (7), Principal Data Scientist (4). Most positions are in Engineering and Research.
Microsoft's active AI hiring is concentrated in: agents (36%), application (21%), serving infrastructure (19%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Microsoft is hiring AI talent in: United States (308 roles), Canada (15 roles), Japan (8 roles), United Kingdom (7 roles).
Job postings at Microsoft most frequently mention: Computer Architecture, Python, Machine Learning, C#, C++.
In the past 30 days, Microsoft has posted 227 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Senior Researcher - Efficient AI Applied research role focused on advancing efficiency across the AI stack for large-scale generative AI experiences in Microsoft 365. The role involves optimizing AI serving systems from algorithmic and systems levels down to hardware and kernel optimizations, with a focus on end-to-end ownership from research to production deployment. | Serve | 9 |
| Principal Researcher Applied research role focused on advancing efficiency across the AI stack (models, ML frameworks, cloud infrastructure, hardware) for generative AI serving systems. The role involves exploring algorithmic, systems, and hardware/software co-design techniques for optimizations like batching, routing, scheduling, caching, and GPU architecture-aware optimizations. Emphasis on end-to-end ownership, driving research through prototyping, validation, and deployment to production for measurable customer impact. | Serve |
| 9 |
| Member of Technical Staff, Capacity & Efficiency Infrastructure - MAI Superintelligence Team 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. | Serve | 9 |
| Member of Technical Staff, Multimodal Infrastructure - MAI Superintelligence Team 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. | ServePost-train | 9 |
| Member of Technical Staff, Software Co-Design AI HPC Systems - MAI Superintelligence Team 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. | ServePretrain | 9 |
| Member of Technical Staff, Software Co-Design AI HPC Systems - MAI Superintelligence Team This role focuses on the co-design and productionization of next-generation AI systems at datacenter scale, optimizing performance, efficiency, and cost across hardware and software. It involves analyzing workloads, driving architectural decisions, optimizing distributed systems for training and inference, and influencing AI hardware design. The role also includes performance modeling, prototyping, and mentoring. | ServePretrain | 9 |
| Senior Researcher - AI Systems - Microsoft Research Senior Researcher in AI Systems at Microsoft Research, focusing on AI infrastructure, ML systems, and HPC systems to improve efficiency and scalability across the full AI model lifecycle, including pre-training, fine-tuning, post-training, and inference. | ServePost-train | 9 |
| Research Intern - LLM Performance Optimization Research Intern role focused on optimizing the performance of Large Language Models (LLMs), involving architecture and inference performance. Requires PhD student status in a STEM field and experience with LLM architecture or inference performance optimization. Preferred qualifications include experience with GPU kernel performance bottlenecks and optimizing compiler architecture. | Serve | 9 |
| Principal Software Engineer, Performance Tooling Principal Software Engineer focused on AI performance tooling and validation for LLMs, including defining technical strategy, architecting benchmarking systems, leading performance investigations, and influencing stakeholders across Microsoft and OpenAI to optimize inference performance and hardware efficiency. | Serve | 8 |
| Senior AI Hardware Architect 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. | ServePost-train | 8 |
| Cambridge Residency Programme: Next-Generation AI Datacentre Networking 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. | Serve | 8 |
| Software Engineer II and Sr. Software Engineer - AI Frameworks 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. | Serve | 8 |
| Software Engineer 2 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. | Serve | 8 |
| Principal Software Engineer - Performance 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. | Serve | 8 |
| Senior Software Engineering 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. | ServePost-train | 8 |
| Principal Software Engineer 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. | ServePost-train | 8 |
| Senior Researcher - Efficient AI 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. | ServePost-train | 8 |
| Principal Software Engineer 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. | Serve | 8 |
| Principal Product Manager - Foundry Inferencing & Training (CoreAI - multiple roles) 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. | ServePost-train | 8 |
| Principal Software Engineer, CoreAI 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. | ServePost-train | 8 |
| Principal Software Engineering - AI Frameworks 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. | Serve | 8 |
| Principal Software Engineer 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. | ServePost-train | 8 |
| Senior Software Engineer, CoreAI Workload Engines 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. | Serve | 8 |
| Principal Software Engineer, CoreAI Workload Engines 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. | Serve | 8 |
| Principal Software Engineer - CoreAI Model Inference & Serving 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. | Serve | 8 |
| Principal Software Engineer, CoreAI 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. | Serve | 8 |
| Member of Technical Staff, AI Systems Engineer - Microsoft Superintelligence 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. | Serve | 8 |
| Senior Researcher - GPU Performance 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. | Serve | 8 |
| Senior AI Software Architect Senior AI Software Architect role focused on optimizing AI model performance and enablement on Maia hardware, involving PyTorch, quantization, parallelization, and inference pipelines. | Serve | 8 |
| Member of Technical Staff, AI Networking - MAI Superintelligence Team 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. | Serve | 8 |
| Member of Technical Staff, Developer Experience - MAI Superintelligence Team 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. | ServeData | 8 |
| Research Intern - AI/ML Numerics & Efficiency Research Intern role focusing on ML systems, numeric precision, data types, and compute technologies for AI workloads at Azure scale. The role involves investigating model efficiency through low-precision formats, quantization, ML kernel development, and benchmarking. It aims to inform decisions on compute platforms, acceleration strategies, and system-level optimizations for training and inference of large-scale models. | Serve | 8 |
| Member of Technical Staff, LLM Inference - MAI Superintelligence Team This role focuses on building and maintaining tools and systems for LLM inference, optimizing compute efficiency, and enabling researchers to run models for various tasks. It involves working with inference frameworks, GPU kernel programming, and distributed systems to improve model performance. | Serve | 8 |
| Research Intern - AI Systems & Architecture Research internship focused on AI systems and architecture, investigating performance modeling, architectural analysis, and emerging inference mechanisms for large-scale AI workloads. The role involves analyzing hardware, software, and model interactions, developing performance models, and prototyping new inference techniques. | Serve | 8 |
| Research Intern - LLM Acceleration Research intern focused on accelerating large language models (LLMs) by optimizing performance on custom architectures, involving computer architecture and parallel programming. | Serve | 8 |
| Research Intern - Systems For Efficient AI Research intern focused on optimizing AI inference systems, including LLM inference, KV caching, request scheduling, and GPU orchestration, to improve latency, throughput, and cost-efficiency. | Serve | 8 |
| Software Engineer 2 Software Engineer to develop AI software for training and deploying advanced AI models, focusing on system software, developer tools, and optimizing large-scale training and inference on novel AI hardware and accelerators. | ServePost-train | 7 |
| Engineering Manager Engineering Manager to lead a team building and operating the cloud brain of Microsoft Defender's real-time protection services. This involves managing ML models, large-scale data platforms, and threat intelligence pipelines that operate at planetary scale with low-latency and high-availability requirements for over a billion users. | Serve | 7 |
| Principal Software Engineer This Principal Software Engineer role focuses on building and managing a hyperscale deployment system, leveraging AI to enhance efficiency, reliability, and automation. The role involves leading a team, mentoring engineers on AI-powered development practices, and driving innovation through AI solutions for deployment intelligence and operational excellence. It requires experience with AI-native development, LLMs, and integrating AI into the software development lifecycle. | Serve | 7 |
| Principal Software Engineer This Principal Software Engineer role focuses on building and operating mission-critical, hyperscale, high-performance, cost-efficient, and compliant AI infrastructure for LLM services within Microsoft 365 and other AI-powered products. The role involves leading the design, implementation, and delivery of an LLM API management service, with a strong emphasis on cost and availability management. | Serve | 7 |
| Principal Group Engineering Manager Principal Group Engineering Manager for AIInfra team at Microsoft, responsible for building and scaling the AI data-plane that powers LLM inferencing workloads across Microsoft and Azure customers. The role involves leading a large team to deliver inference capabilities for a wide range of LLMs with a focus on reliability, efficiency, and ultra-low latency. | Serve | 7 |
| Principal Software Engineer Principal Software Engineer to build and operate mission-critical, hyperscale, high-performance, cost-efficient, and compliant AI infrastructure that powers Microsoft's Large Language Model (LLM) services across Microsoft 365 and other AI-powered products. The role involves leading a team, driving the design and delivery of the AI inferencing platform, and ensuring platform cost efficiency, availability, and operational excellence. | Serve | 7 |
| Software Engineer II Software Engineer II role focused on building and operating mission-critical, hyperscale, high-performance, cost-efficient, and compliant AI infrastructure for LLM services within Microsoft 365 and other AI-powered products. The role involves leading the design, implementation, and delivery of LLM API management services, with a strong emphasis on cost and availability management, and collaboration across product teams. | Serve | 7 |
| Principal Software Engineer The AI Frameworks team at Microsoft develops AI software for training and deploying advanced AI models, focusing on AI compilers and programming abstractions for next-generation supercomputers and AI accelerators. This role involves inventing and implementing compiler features, optimization passes, and code generation for new hardware, as well as optimizing AI workloads using C++ and Python. | Serve | 7 |
| Senior Software Engineer Senior Software Engineer role in Cloud + AI team, focusing on building and optimizing scalable, high-performance AI inference backends and API interfaces for multimodal AI experiences (audio, image, video generation, safety). The role involves full-stack engagement, debugging, and collaboration across teams to deliver end-to-end AI solutions. | ServeAgent | 7 |
| Principal Software Engineering Manager Principal Software Engineering Manager for Microsoft Teams Fundamentals team, focusing on building AI-driven infrastructure to enhance developer productivity. The role involves leading teams, architecting large-scale distributed systems, and leveraging AI/ML for experimentation, pipelines, and developer experiences. Emphasis on operational excellence and applying AI/ML to engineering systems. | Serve | 7 |
| Principal Software Engineering Manager - AI Frameworks This role manages a team focused on optimizing the AI software serving stack, including runtimes, libraries, and APIs, for large-scale model training and inference. The team benchmarks and optimizes LLMs across various hardware, aiming to improve performance, reduce hardware footprint, and enhance Azure's capex efficiency. | Serve | 7 |
| Senior Consultant - Data & AI Senior Consultant role focused on designing and delivering AI-powered data solutions on Microsoft Azure. The role involves leading technical delivery, defining technology strategy, and implementing solutions with an AI-first mindset, acting as a hands-on contributor and potentially leading engineering teams. Emphasizes cloud-native architectures, data engineering principles, and rapid prototyping for production-ready AI solutions. | Serve | 7 |
| Principal Software Engineer Seeking a Principal Software Engineer to design and implement large-scale, high-performance distributed systems for AI model serving. This role involves optimizing inference performance, managing complex infrastructure, and ensuring the reliability and scalability of AI services. | Serve | 7 |
| Principal Group Engineering Manager Principal Group Engineering Manager for Microsoft Defender's real-time protection services, leading a global team to build and scale AI-driven detection capabilities, modernize the protection stack, and unify threat intelligence. The role focuses on operating always-on, low-latency cloud services powered by hundreds of ML models at planetary scale. | ServeData | 7 |