Microsoft

Research Intern role focused on agentic programming, involving building agentic workflows and ML models for software engineering tasks, within a PhD program context.

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 Researcher at Microsoft Research focusing on the intersection of AI systems and society, with an emphasis on sociotechnical approaches to AI evaluation, responsible AI in industry, and AI safety. The role involves interdisciplinary research, collaboration with industry teams, and a strong publication record.

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.

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.

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.

Research role focused on developing and industrializing novel speech algorithms, including integrating speech with LLMs for multimodal modeling, and applying these models to real products with continuous monitoring and evaluation.

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.

Research Intern role focused on advancing the quality, reliability, and evaluation of LLM-based systems by exploring new ML methods for AI assessment and alignment. Responsibilities include co-developing research projects, implementing ML approaches (training/fine-tuning), and developing evaluation frameworks. Requires PhD enrollment in a technical field and hands-on LLM experience.

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.

Research intern to conduct threat modeling for LLM-enabled agentic designs, develop formal security frameworks, and produce security proofs and empirical validations. Deliverable within 12 weeks.

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.

Research intern role at Microsoft Teams focusing on AI-driven innovation in collaboration and communication technologies. Projects may involve AI agents, conversation intelligence, meeting efficiency, and user experience, with a strong emphasis on privacy, security, and accessibility. The role involves applying research methods to real-world challenges in digital collaboration.

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.

Seeking Data Research Engineers to join the Multimodal team, focusing on designing and curating high-quality datasets for next-generation foundation models across vision, language, and audio. The role involves developing data collection strategies, improving dataset quality, analyzing data-driven model behaviors, and building tools for dataset auditing, all within the context of responsible AI practices.

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.

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.

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.

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.

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.

Research Intern position at Microsoft's Sociotechnical Alignment Center (STAC) focusing on evaluating AI systems, particularly generative ones. The role involves applying measurement theory from social sciences and statistics to assess risks, capabilities, and performance. Collaboration with Fairness, Accountability, Transparency, and Ethics in AI (FATE) researchers is expected. The internship emphasizes theoretical and methodological approaches to advance AI system evaluation.

Research intern focused on accelerating large language models (LLMs) by optimizing performance on custom architectures, involving computer architecture and parallel programming.

Research Intern position focused on computer vision and deep learning to improve user understanding of the environment and human-computer interactions. Involves analyzing multimodal sensor data and pushing the state of the art in the field.

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.

Research Intern position at Microsoft Health Futures focusing on applying ML and statistical modeling to understand the human immune system for precision medicine. The role involves building large-scale models of immune responses and immune cells, integrating techniques like representation learning, causal inference, and generative modeling with biological insights.

Research Intern position focused on applying machine learning techniques to biology and healthcare problems, including generative models, computational immunology, and statistical genetics.

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.

Principal Product Manager for Microsoft Dynamics 365 Contact Center, focusing on AI-led voice capabilities. The role involves owning the strategy, vision, and roadmap for AI-powered voice experiences, including cloud telephony, IVR, real-time voice AI, and post-call analytics. It requires collaboration with various Microsoft teams, engagement with strategic customers, and driving go-to-market readiness. The role also involves mentoring other PMs and representing Microsoft externally in the CCaaS and contact center AI space.

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.

Senior Director role focused on leading a team to drive AI adoption within healthcare environments, translating AI capabilities into scalable, real-world use cases and operationalized solutions. This involves people management, customer advocacy, and ensuring solutions align with business goals and company methodologies.

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.

Principal Security Researcher focused on designing and executing advanced adversary simulations using AI-enabled and agentic systems. The role involves evaluating AI-driven detections and defenses, translating attacker tradecraft into actionable insights, and contributing to the development of AI-driven security research workflows.

Principal Security Researcher (Red Team) at Microsoft, focused on using AI and agentic systems to scale offensive security research, adversary emulation, and tooling development. The role involves designing and executing complex attack simulations, developing advanced offensive tools, and evaluating defensive systems, with a strong emphasis on AI-enabled automation and adaptive attack paths.

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 Security Researcher role focused on offensive security research using AI-enabled and agentic systems to emulate real-world cyberattacks. The role involves designing and executing adversary simulations, developing offensive tooling, conducting malware research, and leveraging threat intelligence. A key aspect is utilizing AI and LLM-driven workflows to scale attack development, automation, and simulation fidelity, while partnering with blue teams to improve detections and defensive capabilities.

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.