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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

Senior Software Engineer role focused on optimizing large language model (LLM) deployment on Microsoft's MAIA AI accelerators and GPUs. The role involves building software across the stack, including PyTorch, inference systems (vLLM, SGLang), and performance-critical runtime/kernel components. Responsibilities include architecting tensor computation primitives, extending PyTorch for custom accelerators, improving inference stacks, and optimizing kernels for LLM inference and training workloads.

This role focuses on building and scaling Azure's next-generation Model Router, which involves optimizing and deploying LLMs at a global scale. The responsibilities include applying advanced concepts to product needs, working with large-scale computing frameworks for model improvement, deploying and evaluating models in production, and monitoring their performance over time. The role also involves collaboration with product groups, mentorship, and documentation.

Senior Software Engineer on the AI Frameworks team at Microsoft, focusing on developing AI software that enables running AI models across various devices and hardware. The role involves hands-on software design and development in C/C++ for large-scale model training and inference on novel AI hardware, requiring a strong engineering background and the ability to solve complex technical problems.

The Applied Scientist II will design, develop, and ship AI models into the Teams media stack, focusing on real-time conversation products like Teams. This role involves building end-to-end ML systems, from data cleaning and feature engineering to model training, evaluation, and deployment, with an emphasis on optimizing for performance and memory, and updating deployed models based on A/B testing.

The Principal Software Engineer - Performance Tooling role focuses on optimizing the performance of AI models, particularly LLMs, across various hardware platforms (GPUs, CPUs) and software layers. This involves benchmarking, debugging, profiling, and optimizing for large-scale training and inference to reduce deployment time and hardware footprint, contributing to the efficiency of AI services like Azure OpenAI.

Senior Software Engineer focused on optimizing the inference performance of large language models (LLMs) like those from OpenAI, running on various hardware including GPUs and custom Microsoft silicon. The role involves benchmarking, debugging, and optimizing performance to enable efficient deployment at scale for major Microsoft products and Azure services.

Principal Software Engineer for Microsoft AI's Copilot Discover team, focusing on backend platform for agentic vertical content generation, AI model serving, data ingestion, caching, and serving. Powers content across Microsoft products for over 1B customers. Requires strong distributed systems, cloud storage, and data processing experience.

This role focuses on optimizing the performance of AI inference workloads by exploring micro-architectural innovations and validating end-to-end performance. The Principal Silicon Performance Architect will own performance modeling, analysis, and simulation infrastructure, working closely with chip, system, and software architects to drive data-backed design decisions for improved throughput, latency, and efficiency.

Senior Software Engineer focused on performance tooling for AI frameworks, enabling large-scale training and inference of LLMs on various hardware. The role involves benchmarking, debugging, profiling, and optimizing performance for models like OpenAI's LLMs, aiming to reduce deployment time and hardware footprint.

Senior Software Engineer to work on Azure Translator and Language AI services, focusing on building and scaling large-scale AI systems for natural language processing.

The Azure AI Inferencing team is seeking a Principal Software Engineer to lead the architecture and design of a large-scale, high-throughput, low-latency model-serving platform for Azure OpenAI generative models, supporting billions of requests daily. The role involves end-to-end ownership of solution quality, cross-team collaboration, incident response, and championing security, privacy, and Responsible AI.

Principal Software Engineer to build and scale the core serving systems, request routing, and distribution for all LLMs across Microsoft and Azure customers. The role focuses on delivering inference capabilities reliably, efficiently, and with ultra-low latency for a wide range of AI-powered product experiences.

Principal Software Engineer to design and build a Postgres-based database for modern, AI-native, agent-driven workloads within Microsoft Fabric. The role involves innovating on query planning, execution, and storage layers to support high-performance data access for next-generation applications, leveraging open storage formats and engines.

This role is for a Platform Engineering Manager at Microsoft, focusing on building and scaling the AI platform services that power Copilot, Microsoft's personal AI assistant. The role involves managing a team to develop APIs for finetuning, deployment, and core Copilot experiences, collaborating with AI researchers and product teams to bring AI products to life. The focus is on high-performance, secure, and scalable backend systems for consumer-facing AI products.

Principal Software Engineer role focused on building high-performance software for AI capabilities across Windows & Devices. The role involves architecting and building code for deploying ML models at scale, optimizing edge execution, and guiding system-level decisions for inference, memory, power, and security. It requires defining ML infrastructure strategy and has preferred experience in architecting ML inference pipelines for LLMs, local model integrations, and hardware-aware optimizations.

Full Stack Engineer on the MAI Superintelligence Team focused on ML Efficiency & Observability, building capacity management portals and visibility into model performance for ML researchers and executives. The role involves designing and developing features for user interfaces, integrating with backend APIs for training frameworks, and contributing to internal tooling and infrastructure.

This role focuses on the network architecture for AI accelerator platforms, specifically for high bandwidth and low latency networks critical for AI GPU clusters. The Principal AI Network Architect will evaluate, design, and optimize the network stack from hardware to software kernels, influencing Azure product roadmaps and working with state-of-the-art networking labs. The role requires deep expertise in networking technologies and familiarity with AI model execution pipelines.

Principal Software Engineering Manager to lead a team focused on improving the efficiency, scalability, reliability, and cost of the core infrastructure powering Microsoft 365 Copilot experiences. The role involves acting as a coach, guiding technical design, driving performance optimizations in collaboration with research teams, and ensuring the health and availability of live services.

Principal Software Engineer to advance ad-serving infrastructure, focusing on performance, efficiency, and scalability of next-gen model serving and inference platforms for Ads. Designs and optimizes high-performance serving systems and GPU inference frameworks for deep learning and LLM workloads.

Lead and grow a team building and operating world-class research compute infrastructure, including large-scale GPU clusters and agentic development tools, for Microsoft Research globally.

Senior Software Engineer for Microsoft Copilot's backend platform, focusing on scaling AI services, integrating AI models, and providing tools for engineers. Requires strong backend and cloud infrastructure experience.

Senior 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 scaling the backend platform for Microsoft Copilot, integrating with AI models and empowering Copilot teams. The engineer will design, develop, and maintain performant and secure AI Platform services, ensuring reliability, scalability, and performance. The role requires experience with public cloud infrastructure, containerization, and production software development.

This role is for a Platform Engineering Manager at Microsoft AI, focusing on building and scaling the AI platform services that power Copilot. The role involves managing a team to develop secure, performant APIs for finetuning and deploying core AI experiences, collaborating with various teams, and ensuring high-quality code delivery in a fast-paced consumer-facing environment.

This role focuses on building and operating the foundational GPU accelerated infrastructure for large-scale AI training and inference across Azure. It involves designing systems for GPU management, scheduling, isolation, and sharing, as well as optimizing performance, reliability, and utilization of GPU fleets. The role also requires driving end-to-end platform features, including observability and diagnostics, and influencing platform architecture.

The role focuses on building and scaling the backend platform for Microsoft Copilot, including integrations with AI models and tools for engineering teams. The engineer will design, develop, and maintain performant and secure AI Platform services, ensuring reliability, scalability, and performance. This involves working with public cloud infrastructure, containerization technologies, and production software release.

Firmware Engineer role focused on designing, developing, and debugging firmware for Azure's custom AI accelerator silicon. This involves working across silicon, hardware, and software teams to enable advanced AI workloads and support data center deployment.

The role is for a Site Reliability Engineer (SRE) focused on High Performance Computing (HPC) infrastructure for AI model training and inference. The engineer will ensure the reliability, availability, and efficiency of large-scale distributed AI systems, including GPU clusters, and will be involved in monitoring, automation, incident management, and security.

This role manages a team of Site Reliability Engineers responsible for the reliability and efficiency of large-scale distributed AI infrastructure, specifically for training, fine-tuning, and serving generative AI models. The focus is on leading operations, observability, automation, incident management, and security within hybrid cloud/on-prem CPU+GPU environments, collaborating closely with ML engineers and platform teams.