Principal Research Engineer - Agent 365 Job at Microsoft Corporation (Redmond)

Job Description

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.

Job Responsibility

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
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
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
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
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

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
This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter

Nice to have

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

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