Senior Software Engineer

Azure AI Infrastructure team is looking for passionate engineers to build the largest deep-learning infrastructure service at Microsoft. In this role you will be tasked with building new components to bring the latest innovations in AI Infrastructure onto the Azure AI Platform. You will partner with top engineering talent within Azure AI Infrastructure and across Azure to work on cluster orchestration, job scheduling, storage, networking, containerization and operating system integration. Your work will enable various AI languages and run-times on Azure AI Infrastructure to bring distributed deep learning training and inferencing to life. In addition, you will build infrastructure components required to build, deploy, monitor and service highly available and scalable Microsoft Service Fabric and Kubernetes clusters under your care. You will lead development and customer support from the frontline and establish architecture, service excellence guidelines and a high-quality bar.

Candidates must have a track record for delivering engineering and service excellence on a mid-to-large scale service

Who are We

We are engineers on Azure AI Infrastructure. We believe that building a planet-scale AI Supercomputer from the ground-up which addresses the fundamental pain-points of data scientists and AI practitioners and takes AI to the unprecedented scale is an opportunity of a lifetime. If you share the same dream as us, come join us!

What Is Azure AI Infrastructure

High scale AI workloads are always testing the limits of the infrastructure stack. Large-scale model training and inference with huge data volumes of training data on hundreds-thousands of GPUs make it a true engineering challenge. Azure AI Infrastructure is a globally distributed, multi-tenant service that provides robust, cost-effective and competitive AI infrastructure (compute, networking and storage) for AI training and inferencing. By abstracting workloads from underlying infrastructure, Azure AI Infrastructure creates a shared pool of resources that can be dynamically provisioned for full utilization of expensive GPU compute, and enabling data scientists to productively build, scale, experiment, and iterate their models on top of a robust, performant, scalable and cost-effective distributed infrastructure built for AI. In Azure AI Infrastructure, we are constantly seeking to apply the best ideas from AI, ML, distributed systems, distributed databases, machine learning, information retrieval, networking, and security.