Senior Research Engineer

Role Summary 

As a Research Engineer at Microsoft, you will set the technical vision and lead transformative AI initiatives that shape the future of Microsoft’s products and services. Operating at the intersection of advanced research, engineering, and product strategy, you will drive innovation at scale, architecting solutions that deliver real-world impact for millions of users. You will be a recognised technical leader, influencing cross-organisational strategy, mentoring senior engineers, and representing Microsoft in the global research community.  

Mission & impact:

We are in an era of unprecedented AI innovation. As Microsoft leads the way in foundation models, multimodal systems, and AI agents, our goal is to build an open architecture platform where users can interact with tailored AI agents that drive tangible, real-world outcomes. As a Research Engineer, you will: 

• 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 the state of the art and translate breakthroughs into measurable customer and business impact.

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.

Bringing State-of-the-Art Research to Products 

• Architect and deliver complex 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.
• Build and harden prototypes into production-ready services using robust software engineering and MLOps practices.
• Integrate LLMs, multimodal models, multi-agent architectures, and RAG into Microsoft’s ecosystem.
• Establish best practices 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.

End-to-End System Development 

• ML Design & Architecture: Own end-to-end pipeline from data prep, training, evaluation, deployment, and feedback loops.
• Identify and resolve model quality gaps, latency issues, and scale bottlenecks using PyTorch, or TensorFlow.
• Operate CI/CD and MLOps workflows including model versioning, retraining, evaluation, and monitoring.
• Integrate AI components into Microsoft products in close partnership with engineering and product teams. 

Data-Driven Innovation 

• Evaluation & Instrumentation: Build robust offline/online evals, 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.
• Develop proofs of concept that validate ideas quickly at realistic scales.
• Curate high-signal datasets, including synthetic and red-team corpora, and establish labeling protocols and data quality checks tied to evaluation KPIs. 

Cross-Functional Collaboration 

• Partner with software engineers, scientists, designers, and product managers to deliver high-impact AI features.
• Translate research breakthroughs into scalable applications aligned with product priorities.
• Communicate findings and decisions through internal forums, demos, and documentation. 

Responsible AI & Ethics 

• Identify and mitigate risks related to fairness, privacy, safety, security, hallucination, and data leakage.
• Uphold Microsoft’s Responsible AI principles throughout the lifecycle.
• Contribute to internal policies, auditing practices, and tools for responsible AI.

Operating Altitudes