Senior Research Engineer

As a Senior Research Engineer at Microsoft, you will help advance Microsoft’s mission to empower every person and every organization on the planet to achieve more by building intelligent, scalable cloud services that power Dynamics 365 Contact Center.

This role sits at the intersection of AI, software engineering, and enterprise customer engagement. You will contribute to the design and delivery of AI-first capabilities that enable organizations to connect with, understand, and serve their customers across digital and voice channels.

Mission and Impact

Dynamics 365 Contact Center is a core part of Microsoft’s customer experience platform, enabling enterprises to deliver intelligent, always-on, omnichannel customer support powered by AI and cloud scale.

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.

Build AI-First Contact Center Experiences

Bringing State-of-the-Art Research to Products 

• Design and implement AI systems using foundation models, prompt engineering, retrieval-augmented generation, multi-agent architectures, and classic ML
• Fine-tune large language models on domain-specific data and evaluate via offline and online methods such as A/B testing, telemetry, and shadow deployments
• Build and harden prototypes into production-ready services using robust software engineering and MLOps practices
• 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
• Partner with product teams to improve customer and agent outcomes

End-to-End System Development

• Own features end-to-end from design to live-site operations
  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 Engineering

• 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 

• Paper level (ideas and math): Read, critique, and adapt the latest research; identify gaps; design methods with clear trade-offs and guarantees; communicate complex ideas clearly. 
  Example: “This objective is brittle under our data regime. Here is a tighter analysis and a revised loss we can test this sprint.”
• Code level (implementation): Turn ideas into robust, tested, maintainable modules; integrate with CI/CD; profile and optimize for latency and throughput. 
  Example: “Refactored the prototype into a reusable PyTorch component, added unit tests and benchma