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.