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Principal/Senior Software Engineer, Experimentation Platform - CoreAI

Independently leverage AI tools and practices across the software development lifecycle (SDLC), taking responsibility for AI-generated assets and coaching team members to adopt responsible AI-assisted development practices. Lead b...

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Microsoft Redmond, WA,US, US Source published Jun 29, 2026 Verified 8 hours ago
✓ 95% verification score · Source: Microsoft Careers · Always confirm final requirements on the original source.
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Principal/Senior Software Engineer, Experimentation Platform - CoreAI opportunity at Microsoft
DeadlineSat Dec 26 7:35 PM 2026
EmploymentF U L L T I M E
CountryUS

Overview

Independently leverage AI tools and practices across the software development lifecycle (SDLC), taking responsibility for AI-generated assets and coaching team members to adopt responsible AI-assisted development practices. Lead by example to produce extensible, maintainable, well-tested, secure, and performant code; apply metrics to drive code quality and stability, and continuously improve code performance, testability, and cost-effectiveness across the team. Own and drive the architecture and design of product components, creating design specifications, and ensuring system architecture meets performance, scalability, resiliency, and disaster recovery requirements with minimal technical oversight. Collaborate with partner teams, PMs, and subject matter experts (privacy, security, SRE) to determine customer requirements, incorporate feedback, and deliver scalable, reliable features with

Full job description

Full Job Description

Independently leverage AI tools and practices across the software development lifecycle (SDLC), taking responsibility for AI-generated assets and coaching team members to adopt responsible AI-assisted development practices. Lead by example to produce extensible, maintainable, well-tested, secure, and performant code; apply metrics to drive code quality and stability, and continuously improve code performance, testability, and cost-effectiveness across the team. Own and drive the architecture and design of product components, creating design specifications, and ensuring system architecture meets performance, scalability, resiliency, and disaster recovery requirements with minimal technical oversight. Collaborate with partner teams, PMs, and subject matter experts (privacy, security, SRE) to determine customer requirements, incorporate feedback, and deliver scalable, reliable features with proper end-to-end testing. Drive engineering excellence through automation, tooling improvements, security best practices, and deployment infrastructure. Maintain operations of live site services on a rotational on-call basis, implement solutions to complex live-site issues, conduct and present incident postmortems, and proactively improve troubleshooting guides, telemetry, and monitoring to reduce incident volume. Experience with AI-assisted development workflows and responsible use of AI coding tools in production environments. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. These requirements include, but are not limited to the following specialized security screenings: Master'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, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience building and operating large-scale distributed systems on cloud platforms (Azure, AWS, GCP), including design, deployment, monitoring, and troubleshooting of production workloads. Experience designing and implementing service architectures with strong emphasis on scalability, reliability, fault tolerance, and cost optimization. Experience using observability tools (logging, metrics, distributed tracing) to diagnose complex service issues and drive systemic reliability improvements. Experience mentoring engineers, driving code reviews, and raising engineering best practices within a team. Familiarity with experimentation platforms, A/B testing methodologies, and statistical analysis of product metrics. Experience with AI-assisted development workflows and responsible use of AI coding tools in production environments.

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