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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

This role focuses on building and improving AI agent platforms, specifically the Agent Performance team within Azure AI Platform. The core responsibility is to integrate scientific advancements, particularly RL techniques like RLHF, into production-ready features for AI agents. The role involves monitoring, evaluating, optimizing, and enabling self-improvement of these agents, bridging the gap between research and product.

Principal Software Engineer at Microsoft Quantum focused on building system software for quantum computers, integrating AI, and developing novel algorithms for computationally hard problems. The role involves designing, implementing, and optimizing software and firmware subsystems for quantum hardware, with a focus on quantum error correction and real-time quantum stacks.

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.

The Signals Modeling team builds core intelligence for predicting user interaction with ads, designing and training large-scale transformer models for ad ranking, pricing, and optimization. They own end-to-end ML systems, from data construction to deploying models that drive revenue and ROI in a massive ads ecosystem.

Full Stack Engineer to build LLM-powered data engineering experiences and infrastructure for Microsoft Fabric. The role involves implementing agentic workflows and scalable LLM-backed data features, focusing on AI Engineering and modern LLM-based systems.

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 role in Microsoft's CoreAI division, focusing on building AI-powered developer tools and planet-scale platforms with enterprise trust, security, and reliability. The role involves designing, building, and operating core platform services for developers across the entire application lifecycle, including AI-native engineering practices and AI-enabled systems to improve various aspects of the software development lifecycle.

Software Engineer II role focused on building foundational platforms for intelligent agents and generative AI systems within Microsoft Foundry. The role involves developing large-scale, cloud-native systems for the end-to-end agent lifecycle, including secure enterprise deployment, tool integration, model fine-tuning, and production observability/evaluation. It sits at the intersection of distributed systems, AI infrastructure, and developer platforms.

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.

This role focuses on delivering enterprise AI solutions using Azure OpenAI, Azure AI Services, and custom machine learning. It involves designing and implementing secure, advanced agentic AI systems and optimized RAG architectures, requiring expertise in full-stack application engineering, cloud-native architecture on Azure, and DevOps. The role also emphasizes solutioning, pre-sales, and technical leadership, with a requirement for relevant Azure AI certifications.

Senior Consultant role focused on designing and delivering end-to-end, industry-aligned, AI-powered cloud-native applications for enterprise customers. Combines full-stack engineering, applied AI, and Azure cloud expertise with a consulting mindset to drive business outcomes and digital transformation.

Principal Software Engineer in the CoreAI organization at Microsoft, focusing on building and integrating Responsible AI services. The role involves developing customer-facing, high-performance, low-latency, and high-availability AI services, improving AI tools across the SDLC, and ensuring Responsible AI controls and engineering-health metrics are embedded. Responsibilities include coding, system design, driving engineering excellence, implementing AI features with guardrails, and ensuring reliability and supportability.