Senior Principal Research Engineer at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

09 Mar, 26

Salary

296400.0

Posted On

09 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

C, C++, C#, Java, JavaScript, Python, Large Language Models, Backend Services, APIs, Distributed Systems, Cloud-Based Services, Responsible AI, A/B Testing, Telemetry, Monitoring, Evaluation Pipelines, Model Routing

Industry

Software Development

Description

Overview Copilot usage is growing rapidly across Microsoft 365 and custom agent experiences, requiring scalable and resilient AI systems. This role drives model choice across multiple providers and modalities, ensuring quality, safety, latency, and cost are balanced while reducing vendor lock‑in. As a Senior Principal Research Engineer, you will set technical vision and build backend systems, APIs, and evaluation pipelines that enable intelligent model routing for Copilot and Copilot Studio. You’ll partner with PMs, scientists, and reliability engineers to instrument quality signals, govern rollouts, and create decision frameworks for key scenarios like authoring, reasoning, retrieval‑augmented generation, and multi‑agent orchestration. You will productize responsible AI metrics (e.g., groundedness, coherence, relevance) into dashboards for makers and platform teams, influence cross‑org strategy, mentor senior engineers, and represent Microsoft in the global research community—delivering innovation at scale for millions of users 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. Responsibilities Build multi provider model routing services with policy driven selection, traffic splitting, and observability. Develop evaluation pipelines for offline and online experiments; automate metrics like task success, groundedness, latency, and cost. Operationalize model comparisons through scorecards, dashboards, and A/B testing for rollout decisions. Harden backend infrastructure for scale, security, and compliance; implement caching, rate limiting, and safety filters. Instrument telemetry and monitoring using OpenTelemetry; enable RCA and performance insights. Collaborate cross functionally with PMs, scientists, and Responsible AI teams; document and evangelize best practices. Qualifications Required Qualifications: 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. 1+ years of experience architecting and deploying Large Language Models (LLMs) in production at scale. Other Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Preferred Qualifications: Master's Degree in Computer Science or related technical field AND 12+ 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 15+ 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 backend services or APIs in a production environment using at least one modern programming language (e.g., C#, Java, Python, or TypeScript). Experience deploying or operating distributed systems or cloud-based services (e.g., Azure, AWS, or GCP) in a production environment. Familiarity with Responsible AI frameworks and bias-mitigation techniques. Demonstrated ability to shape product strategy and drive organizational change. Experience with Microsoft’s LLMOps stack: Azure AI Foundry, Azure Machine Learning, Semantic Kernel, Azure OpenAI Service, and Azure AI Search for vector/RAG PhD in AI/ML or related fields with top-venue publications and/or patents. #BICJOBS #MCSJobs Software Engineering IC6 - The typical base pay range for this role across the U.S. is USD $163,000 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

The role involves building multi-provider model routing services and developing evaluation pipelines for experiments. It also includes collaborating with cross-functional teams to instrument quality signals and govern rollouts.