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.

This role focuses on designing, developing, and operating large-scale reinforcement learning systems for training agents and reasoning models. It involves contributing to cutting-edge research and bridging the gap between research and production-grade distributed systems, with responsibilities including tuning pretraining software for specific GPU architectures and contributing to AI model development.

Technical Program Manager for Microsoft AI's Superintelligence Team, focused on training frontier AI models. The role involves end-to-end program planning, coordinating with researchers and engineers, managing timelines, mitigating risks, and ensuring the responsible advancement of AI.

This role is focused on building and advancing large-scale foundation models, with a specific emphasis on multimodal capabilities and ensuring AI systems are controllable, safety-aligned, and anchored to human values. The position involves algorithm development, model architecture design, experimentation, data pipeline innovation, and improving training/deployment efficiency, aiming to push the frontier of AI responsibly.

This role is focused on training frontier AI foundation models at Microsoft AI, specifically within the Pre-Training team of the Superintelligence Team. The responsibilities include developing algorithms, model architectures, data mixtures, and scaling laws for large-scale training, driving implementations, conducting experiments, and overseeing training runs. The role emphasizes collaboration with infrastructure, data, post-training, and multimodality teams.

This role focuses on building and optimizing the software stack for massive GPU clusters, high-throughput storage systems, and cutting-edge AI research. You will work closely with model scientists to scale up the latest research recipes, implement new forms of distributed training parallelism, and ensure the reliability and performance of thousands of GPUs across our supercomputing fleet. Profiling, benchmarking, debugging, and fine-grained optimization are core to this role, demanding both engineering rigor and creativity.

Software Engineer role focused on building and managing large-scale GPU infrastructure and training/inference platforms for AI models (LLMs, SLMs, multimodal, code-specific) on Azure and partner clouds. Responsibilities include architecting, designing, and developing core AI infrastructure services, collaborating with researchers, and enhancing system stability, latency, security, and maintainability for training runs.

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.