Microsoft

Seeking engineers and researchers to join the Pretraining Text Data team to build the next generation of foundation large language models. The role focuses on designing and curating high-quality datasets, developing novel data collection strategies, improving dataset quality and integrity, understanding data-driven model behaviors, training models on data impact, and aligning datasets with ethical and societal values. This is a cross-disciplinary, high-impact role at the intersection of data and innovation.

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.

The AI Data team at Microsoft AI is building the world's most advanced multimodal dataset to power frontier AI models. This role focuses on designing and developing data pipelines for massive multi-modal training data (text, audio, images, video) and building infrastructure to store and process petabytes of data. The team partners with pre-training and post-training teams to improve data recipes through experimentation and collaborates with product teams to identify model gaps.

This role focuses on building and optimizing training data pipelines and evaluation frameworks for audio models, with a secondary focus on model tuning and deployment. The engineer will be responsible for data sourcing, cleaning, labeling, QA, versioning, and lineage, as well as developing evaluation frameworks and driving continuous quality improvements. They will also optimize training and inference performance for latency, throughput, cost, and reliability.

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.

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.

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.

Principal Machine Learning Engineer to work on data labeling and classification for large-scale multimodal Copilot data within Microsoft AI. The role involves prototyping and productionizing classification flows, operating prompted classifiers, and building secure data-labeling pipelines. Requires experience in data pipelines, data science, ML, and strong communication skills.

Microsoft AI is seeking an experienced Data Engineer to build mission-critical data pipelines for their Copilot systems. The role involves ingesting, processing, and publishing data streams to support AI model fine-tuning and introspection. The ideal candidate will have strong big data processing skills and experience with cloud infrastructure.

This role is for a Data Engineering Manager within Microsoft AI's Copilot team. The primary responsibility is to build and manage scalable data pipelines that ingest, process, and publish data streams for AI use cases, specifically for fine-tuning models and improving human-AI interactions. The role involves managing a team of data engineers and ensuring operational excellence for the data platform.

This role focuses on building and architecting distributed data platforms and pipelines that process massive datasets for AI models, including training, inference, and evaluation. It involves designing event-driven architectures, handling unstructured data, engineering feedback loops for AI, and optimizing compute for cost and performance.

Backend Engineer role focused on building the data foundations for Copilot's memory and personalization features. This involves designing and operating large-scale data pipelines for signal ingestion, normalization, enrichment, aggregation, and memory generation, enabling downstream ML and product consumers. The role also involves technical leadership in data systems and influencing architecture.

The AI Data Infra team at Microsoft AI is responsible for building data infrastructure to help MAI teams to generate the biggest and best training dataset. This role focuses on designing and developing data pipelines for multimodal training data and owning critical data infrastructures like Spark, Ray, and Vector Databases.

The role focuses on building and maintaining data infrastructure for large-scale AI model training, specifically ingesting and processing enormous amounts of multimodal data (text, audio, images, video). This includes owning data pipelines, Spark, Ray, and Vector Databases, and partnering with pretraining and post-training teams to improve the data recipe for frontier models.

Consultant role focused on delivering end-to-end data and AI solutions using Microsoft technologies. Responsibilities include solution design, development, deployment, and ensuring customer value through technical expertise and customer focus. The role involves working with Azure Data Services, AI/ML foundations, and operationalizing ML pipelines.

Senior Consultant Data & AI at Microsoft, focusing on delivering end-to-end data and AI solutions for clients. Responsibilities include designing, developing, and deploying solutions on Microsoft technologies, building AI-powered data pipelines, feature engineering, operationalizing ML pipelines, and working with LLMs, NLP, Computer Vision, RAG, and Gen AI frameworks. The role emphasizes Azure Data Services, Data Warehouse, and MLOps concepts.

Consultant role focused on delivering end-to-end data and AI solutions using Microsoft technologies. The role involves designing, developing, and deploying solutions, with a focus on data engineering, ML-ready datasets, and operationalizing ML pipelines on Azure. It requires strong foundational knowledge of Azure Data Services, AI/ML concepts, and experience with AI-powered data pipelines.

This Principal Data Scientist role focuses on leveraging AI tools to accelerate workflows and amplify team productivity within Microsoft's CoreAI Growth and Data Science team. The role involves delivering insights, reporting, models, and tools to guide product strategy and drive growth across the developer ecosystem. Key responsibilities include providing critical analyses to executives, driving alignment across stakeholders, identifying high ROI data opportunities, contributing to data insight scaling infrastructure and culture, mentoring, and operationalizing models and reporting. While the role uses AI tools, its primary focus is on data science and analytics to support product strategy and growth, rather than building core AI models.

Data Engineer role focused on building and operating data ingestion, transformation, and validation pipelines that power experimentation, insights, and AI features for SwiftKey. The role involves working with large-scale data systems in cloud environments, ensuring data quality, and supporting AI feature development.

This Principal Software Engineer role focuses on building and operating large-scale, real-time data pipelines and feature/embedding materialization systems for Microsoft Ads. The role involves designing and implementing streaming ETL, managing messaging systems like Kafka, ensuring data contracts, and integrating with ML inference serving. Success metrics include freshness, correctness, latency, reliability, and cost. The role requires strong programming skills, experience with distributed systems, and observability.

Senior Data Scientist role within Microsoft's Industry Solutions Engineering (ISE) team, focusing on developing AI and cloud-based solutions for customers. The role involves data acquisition, dataset development, model evaluation, and building operational models at scale, working collaboratively with customers and internal product teams.

Software Engineer on a data engineering team that builds and manages large-scale, real-time data platforms and analytics systems. The role focuses on transforming raw signals into trusted, high-quality data products used for automation, decision-making, and AI-driven workflows, enabling agentic automation for infrastructure services. The team uses modern data mesh architectures and emphasizes reliability, governance, and observability.

This role focuses on designing and implementing large-scale data pipelines and models for an AI infrastructure platform, processing terabytes to petabytes of data daily. The engineer will ensure data correctness, reliability, and usability for experimentation and analytics, working closely with data scientists and platform engineers.

Software Engineer II on the Microsoft Fabric Data & Telemetry team, responsible for building and maintaining telemetry instrumentation services and pipelines, developing tools for metadata management, and creating job monitoring and notification services for data pipelines. The role focuses on providing high-quality data for running the service and business, supporting pillars like live-site monitoring, diagnostics, and analytics for product development, and ensuring customers have data for their own observability needs. The team applies classic cloud and software engineering practices alongside big data-engineering and analytical practices.

Senior Software Engineer role at Microsoft Azure Data engineering team, focusing on designing and building internal tools, engineering systems, and automation capabilities to enhance the quality and reliability of Power BI Desktop and Service. The role involves end-to-end tool development, including architecture, backend services, dashboards, telemetry integration, and AI-powered workflows, with a focus on integrating GenAI into developer workflows and modernizing engineering processes.