This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Bing Sports builds experiences for live scores, schedules, standings, news, and sports knowledge across Bing. As a Principal Software Engineer, you will set technical direction and deliver high-scale, low-latency services and experiences, partnering with product, data science, design, and engineering teams. You’ll be a hands-on leader who drives architecture, raises quality, mentors engineers, and ensures reliability and operational. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
Job Responsibility:
Lead end-to-end design and delivery for Bing Sports components (APIs, services, pipelines, integrations) from concept to production and sustainment
Define architecture for scalable distributed systems
set SLOs/SLIs
build observability and on-call readiness
Set engineering standards and lead design/code reviews for security, reliability, performance, testing, and maintainability
Partner with PM to translate customer needs into technical roadmaps and investment sequencing (features, platform, tech debt)
Build and improve sports data ingestion/normalization with data quality, provenance, and monitoring
Improve answer quality via experimentation and evaluation
collaborate with data science on model/service integration
Optimize latency and cost through performance tuning, caching, and storage/indexing choices