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.

Microsoft AI is seeking individuals to train the world's most capable AI frontier models, focusing on scale, performance, and product deployment. The role involves developing algorithms, model architectures, and data mixtures for large-scale training, driving implementations, conducting experiments, and collaborating with infrastructure, data, post-training, and multimodality teams.

Develops and ships post-training methods for large language models (LLMs) used in Microsoft Copilot, focusing on data collection, evaluation, finetuning algorithms, and prototyping new capabilities. Collaborates with pretraining and product platform teams to deploy models and improve them based on user feedback.

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 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.

Forward Deployed Engineer (FDE) role focused on building and deploying AI-powered solutions for customers, working at the intersection of advanced AI technology and real-world business impact. Responsibilities include engineering data pipelines, building custom applications, deploying scalable workflows, and monitoring/refining solutions. Requires practical experience with AI/LLMs in production and leveraging generative AI.

This role is focused on ensuring the reliability, performance, and availability of Microsoft's large-scale AI training infrastructures, which involve tens of thousands of GPUs and advanced networking. The responsibilities include designing transport, fabric architecture, telemetry, observability, and automated troubleshooting for these clusters. The role also involves AI training and inference cluster bring-up, performance benchmarking, and root-cause analysis, with a goal of developing predictive health models and autonomous remediation systems.