Principal Software Engineering Manager, Coreai

Microsoft Microsoft · Big Tech · Redmond, WA +4 · Software Engineering

Principal Software Engineering Manager for Microsoft's CoreAI organization, focusing on building and scaling Responsible AI services. The role involves leading a team of engineers to develop customer-facing AI services with a focus on high performance, low latency, and high availability, managing the end-to-end development lifecycle and production readiness.

What you'd actually do

  1. Lead a team of software engineers, hiring, growing, and upskilling their talent
  2. Review the design and code artifacts that make up large-scale distributed cloud services and solutions with a focus on high availability, scalability, robustness, and observability.
  3. Lead project development across the organization and work with subject matter experts and stakeholders to drive development and release plans.
  4. Ensure systems owned by the team meet business requirements and service KPIs.
  5. Take end-to-end responsibility for the development lifecycle and production readiness of the services the team builds and drive the team’s DevOps culture.

Skills

Required

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

Nice to have

  • Master's Degree in Computer Science or related technical field
  • 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • 5+ years of directly managing software engineers building backend services, cloud infrastructure, or platforms
  • 5+ years technical engineering experience designing and delivering highly available, large-scale cloud services and distributed systems.
  • 4+ years of technical engineering experience with machine learning model infrastructure, hosting, and operations.
  • Ability to navigate the company and influence and inspire peers in engineering and broad product development.
  • Demonstrate depth of knowledge and understanding of software architecture, design tradeoffs, and practices of mature DevOps culture.
  • The track record of pursuing and delivering innovative insights that translate to value generation.

What the JD emphasized

  • Responsible AI risks
  • high performance, low latency, and high availability
  • machine learning model infrastructure, hosting, and operations

Other signals

  • Responsible AI
  • end-to-end AI stack
  • large-scale distributed cloud services
  • high availability, scalability, robustness, and observability
  • machine learning model infrastructure, hosting, and operations