Senior And/or Principal Software Engineer - AI Frameworks Networking, Systems and Tools

Microsoft Microsoft · Big Tech · United States · Software Engineering

This role focuses on building the software infrastructure and systems for running AI models across various devices, optimizing both training and inference. It involves deep systems programming, networking, and integration with AI hardware and Azure.

What you'd actually do

  1. Apply strong engineering principles for defining robust and maintainable architectures and designs.
  2. Collaborate broadly across multiple disciplines from hardware designers to ML developers.
  3. Help establish and drive the adoption of good coding standards and patterns.
  4. Perform software development in C/C++, C#, Python, and other languages.
  5. Identify requirements, scope solutions, estimate work, schedule deliverables.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field
  • 4+ years technical engineering experience
  • coding in languages including, but not limited to, C++, C, or Python

Nice to have

  • Master's Degree in Computer Science or related technical field
  • 8+ years technical engineering experience
  • coding in languages including, but not limited to, C++, C, or Python
  • 3+ years of experience in systems programming (C, C++, Rust, C#, or similar)
  • focus on low-level or performance-critical software
  • Solid understanding of memory models, concurrency, and interprocess communication
  • Experience working on infrastructure involving hardware interfaces or device communication (e.g., PCIe, DMA, RDMA, or similar)
  • Familiarity with Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), or other accelerator architectures and their runtime systems
  • Experience implementing communication protocols or working with driver/kernel interfaces
  • Exposure to observability or profiling tools (e.g., eBPF, trace buffers, performance counters, telemetry hooks)

What the JD emphasized

  • systems programming
  • low-level or performance-critical software
  • hardware interfaces or device communication
  • accelerator architectures
  • communication protocols or working with driver/kernel interfaces
  • observability or profiling tools

Other signals

  • AI models everywhere
  • optimize and scale out model training and inference
  • AI hardware, systems, and software
  • systems stack, related tools and its integration with Azure
  • software development kit (SDK)