Principal Software Engineer - Responsible AI (coreai)

Microsoft Microsoft · Big Tech · Mountain View, CA +4 · Software Engineering

Principal Software Engineer focused on building and integrating Responsible AI services, ensuring high availability, scalability, and low latency for customer-facing AI applications within Microsoft's CoreAI organization. This role involves end-to-end ownership of the development lifecycle, evaluating architectures, and upholding software engineering best practices.

What you'd actually do

  1. Design, and develop large-scale distributed cloud services and solutions with a focus on high availability, scalability, robustness, and observability.
  2. Lead project development across the organization and work with subject matter experts and stakeholders to drive development and release plans.
  3. Evaluate alternative architectures and technologies that best fit the business requirements and service KPIs.
  4. Take end-to-end responsibility for the development lifecycle and production readiness of the services you build and drive the team’s DevOps culture.
  5. Drive and uphold the best practices of modern software engineering through code and design reviews and take effective service decisions based on data and telemetry.

Skills

Required

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

Nice to have

  • 6+ years of 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 development, release, 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.
  • 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
  • design of new AI services
  • integration with existing services
  • machine learning model development, release, and operations

Other signals

  • Responsible AI services
  • customer-facing AI services
  • scalable and sustainable architecture
  • high performance, low latency, and high availability
  • design of new AI services and integration with existing services