Principal Software Engineer

Microsoft Microsoft · Big Tech · Mountain View, CA +1 · Software Engineering

Principal Software Engineer on the Azure Artificial Intelligence Core team, responsible for designing, building, and maintaining AI systems powering large-scale multimodal workloads. This role involves end-to-end ownership from API interfaces to inference backends, with a focus on safety, real-time audio interaction, image/video generation, and benchmarking/optimizing LLM performance. Collaboration with OpenAI and internal teams is key.

What you'd actually do

  1. Architect and Implement AI Systems: d esign and build scalable, reliable architectures that meet high-performance requirements for AI systems including engine, API... Apply deep knowledge of distributed systems principles to create robust solutions.
  2. Debug and Optimize Across the Stack: dive deep into existing components to identify and resolve issues quickly. Ensure fast turnaround for critical fixes while maintaining system integrity.
  3. Collaborate Across Teams and Partners: work closely with multiple internal teams and external partners to solve complex problems, align on technical decisions, and deliver integrated solutions.
  4. Innovate and Drive Technical Excellence: i dentify non-obvious technical approaches that unlock new possibilities. Architect and execute features with measurable goals, iterating rapidly to achieve outcomes.
  5. Full-Stack Ownership: e ngage with components across the entire stack—from infrastructure to APIs—to deliver end-to-end solutions.

Skills

Required

  • C
  • C++
  • C#
  • Java
  • JavaScript
  • Python
  • distributed systems
  • scalable architecture
  • API designs
  • AI/ML fundamentals
  • Kubernetes
  • Azure Devops
  • Docker

Nice to have

  • gRPC
  • FastAPI
  • Autogen
  • Semantic Kernel
  • LangChain
  • OpenAI API
  • Azure OpenAI
  • technical leadership
  • mentoring
  • Certifications
  • open-source contributions
  • published research
  • Industry recognition
  • thought leadership

What the JD emphasized

  • designing, building, and maintaining distributed systems with scalable architecture, and API designs
  • architecting and delivering AI-based solutions
  • safety stack
  • real-time APIs
  • audio-based conversations with large language models
  • benchmark OpenAI and other LLM models for performance on GPUs and Microsoft HW
  • debug and optimize our stacks for reliability and latency

Other signals

  • design and build scalable, reliable architectures that meet high-performance requirements for AI systems including engine, API
  • architecting and delivering AI-based solutions
  • collaborate closely with OpenAI and internal partners to advance cutting-edge capabilities
  • safety stack, providing content moderation across different verticals
  • real-time APIs, enabling audio-based conversations with large language models
  • benchmark OpenAI and other LLM models for performance on GPUs and Microsoft HW
  • debug and optimize our stacks for reliability and latency