Principal Research Engineer - Agent 365

Microsoft•Redmond, WA

About The Position

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.

Requirements

Bachelor'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 equivalent experience.
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.

Nice To Haves

Bachelor’s Degree in CS/EE/Math or related field 10+ years in applied AI/ML research and product engineering,
OR Master’s Degree+ 5 years in applied AI/ML research and product engineering,
OR PhD + 2 years in AI/ML or related field with a strong publication record.
PhD in AI/ML or related field with top-venue publications and/or patents.
Experience architecting and deploying LLMs/multimodal models and multi-agent systems in production at scale.
Familiarity with Responsible AI frameworks and bias-mitigation techniques.
Experience shaping product strategy and driving 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.
Experience leading large-scale AI systems and cross-org initiatives that shipped.
Experience with software engineering foundations and Python plus deep-learning frameworks (PyTorch/ TensorFlow) and modern MLOps/tooling.
Experience mentoring engineers/researchers and influencing product direction through data and experimentation.

Responsibilities

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: Turn ideas into tested, maintainable modules (e.g., refactor prototypes into reusable PyTorch components; integrate CI/CD; cut latency by double-digit %).
Systems & GPU Reality: Optimize distributed training/inference, GPU utilization, memory, and data throughput; engineer pragmatic interop across stacks (e.g., Python ML with C# services) to balance accuracy, latency, and cost.
Embody our culture and values.