Senior Software Engineer - Responsible AI (coreai)

Microsoft Microsoft · Big Tech · Mountain View, CA +2 · Software Engineering

Senior Software Engineer focused on building Responsible AI services, including identifying, measuring, mitigating, and monitoring AI risks across various content types. The role involves designing and developing large-scale distributed cloud services with a focus on safety, governance, inference, evaluation, and multimodal safety infrastructure.

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. Take end-to-end responsibility for the development lifecycle and production readiness of the services you build and drive the team’s DevOps culture.
  4. 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.
  5. Understand Microsoft businesses and collaborate with stakeholders towards cohesive, end-to-end experiences for Microsoft customers.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field
  • 4+ 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

  • 4+ years of technical engineering experience designing and delivering highly available, large-scale cloud services and distributed systems.
  • 2+ years of technical engineering experience with machine learning model development, release, and operations.
  • Demonstrate depth of knowledge and understanding of software architecture, design tradeoffs, and practices of mature DevOps culture.
  • Experience using appropriate artificial intelligence (AI) tools and practices across the software development lifecycle (SDLC) in a disciplined manner.
  • Experience in any one or more of the following areas: Safety and governance platforms for AI models and agents, Inference, routing, orchestration, and policy enforcement systems, Evaluation, red teaming, and monitoring infrastructure for AI systems, Deployment automation, CI/CD, and compliance tooling (e.g., zero‑manual‑effort deployments), Multimodal safety infrastructure (image, video, audio, provenance), Agent governance and control‑plane capabilities

What the JD emphasized

  • Responsible AI
  • customer-facing AI services
  • scalable and sustainable architecture
  • high performance, low latency, and high availability
  • industry's best Responsible AI services
  • design of new AI services and integration with existing services
  • machine learning model development, release, and operations
  • Safety and governance platforms for AI models and agents
  • Inference, routing, orchestration, and policy enforcement systems
  • Evaluation, red teaming, and monitoring infrastructure for AI systems
  • Multimodal safety infrastructure (image, video, audio, provenance)
  • Agent governance and control‑plane capabilities

Other signals

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