Principal Research Engineer - Agent 365

Copilot usage is growing rapidly across Microsoft 365 and custom agent experiences, requiring scalable and resilient AI systems.  

As a Principal Research Engineer at Microsoft, you will set the technical direction and lead transformative AI initiatives that shape the future of Microsoft’s products and services. Operating at the intersection of research, engineering, and product strategy, you will drive innovation at scale, architecting solutions that deliver real-world impact for millions of users. You will influence cross-organizational strategy, mentor engineers, and represent Microsoft in the global research community. 

Mission & Impact 

• Define and execute technical strategy for foundational models, multi-agent systems, and next-generation Copilot experiences, especially within Business & Industry Copilot.
• Lead cross-team efforts to deliver scalable, reliable, and responsible AI systems.
• Advance state-of-the-art technology and communicate breakthroughs into measurable customer and business impact. 

Why Microsoft? 

By joining Microsoft, you become part of a team at the forefront of AI innovation. You will have the opportunity to lead transformative projects, shape industry standards, and empower billions 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. Every day, we uphold our values of respect, integrity, and accountability to nurture an inclusive culture where everyone can thrive at work and beyond. 

Technical Leadership & Vision

• Architect and deliver AI systems across model development, data, infra, evaluation, and deployment spanning multiple product lines.
• Set technical direction for large programs; drive alignment across Research, Engineering, and Product.
• Integrate LLMs, multimodal models, multi-agent architectures, and RAG into Microsoft’s ecosystem.
• Establish standards for MLOps, governance, and Responsible AI, compliant with Microsoft principles and industry standards.

Innovation, Research & Translation

• Drive original research and thought leadership (whitepapers, internal notes, patents); convert insights into shipped capabilities.
• Research Translation: Continuously review emerging work; identify high-potential methods and adapt them to Microsoft problem spaces.
• Production Integration: Turn research prototypes into production-quality code optimized for scale, latency, and maintainability.
• ML Design & Architecture: Own end-to-end pipeline from data prep, training, evaluation, deployment, and feedback loops.
• Evaluation & Instrumentation: Build offline/online evaluations, experimentation frameworks, and telemetry for model/system performance.
• Learning Loop Creation: Operationalize continuous learning from user feedback and system signals; close the loop from experimentation to deployment.
• Experimentation & E2E Validation: Design controlled experiments, analyze results, and drive product/model decisions with data.
• Model Optimization: Select and pursue the right leaderboards and benchmarks for our problem domain; tune/extend models and ensure they translate to successful UX and production metrics.

Cross-Functional Collaboration & Influence

• Broker collaborations across Microsoft Research, product engineering, and external partners.
• Mentor and develop engineers and researchers; foster a culture of technical excellence and innovation.
• Communicate technical vision and results to executives, internal forums, and external audiences.

Responsible AI & Ethics

• Establish fairness, privacy, and safety of end-to-end, design, data, training, evaluation, deployment, and monitoring.
• Create and drive adoption of internal policies, auditing frameworks, and tools for ethical AI at scale.

Operating Altitudes

• Business Initiatives & Customer Outcome: Start from the “why.” Frame business needs into technical requirements and evaluate impact (e.g., reducing false positives that cost customers).
• Paper-Level Ideas & Math: Read and advance reason about guarantees and trade-offs; publish and teach.
• Code-Level Implementation: