Software Engineering, Coreai

Microsoft Microsoft · Big Tech · Redmond, WA +2 · Software Engineering

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.

What you'd actually do

  1. Architect, design, and develop core AI Infrastructure services developed in Go, Rust, Python, C++, and C# deployed on large-scale Kubernetes clusters to support pre-training and post-training of state-of-the-art LLMs, SLMs, multimodal, and code-specific models.
  2. Collaborate closely with engineers, researchers and external partners to debug, diagnose, and improve stability of large-scale training runs.
  3. Enhance systems and applications to deliver high stability, low latency, strong security, and maintainability in large-scale complex training environments in Azure and in partner clouds.
  4. Provide operational support, technical leadership, and vision while contributing to the deployment, monitoring, and continuous improvement of engineering systems and practices.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field
  • 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, Python, or equivalent experience.
  • Ability to meet Microsoft, customer and/or government security screening requirements

Nice to have

  • 2+ years designing, developing, and shipping high quality software.
  • 2+ years of experience with distributed systems and cloud-based infrastructure.
  • 1+ year of experience with DevOps practices (CI/CD, automated testing, deployment, etc.).
  • 2+ years of software development experience in C#, C++, Python, or similar languages.
  • 2+ years of experience with containerization tools (e.g., Docker, Kubernetes).
  • Knowledge and hands on experience with production ML systems, large-scale training infrastructure, NCCL, CUDA libraries and tools.

What the JD emphasized

  • large-scale Kubernetes clusters
  • large-scale training runs
  • large-scale complex training environments

Other signals

  • large-scale training infrastructure
  • GPU management
  • inference and training platforms
  • LLMs, SLMs, multimodal, and code-specific models