As a Senior Research Engineer at Microsoft, you will advance Microsoft’s mission to empower every person and every organization to achieve more. You will help build and integrate cutting-edge AI into Microsoft products and services within Experience + Devices (E+D) org, ensuring solutions are inclusive, ethical, and impactful. This role blends applied research, machine learning engineering, and product innovation. You will lead efforts to ship reliable, production-grade AI systems across the stack, from model development and fine-tuning to performance optimization and deployment.

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

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

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 benchmarks, and cut P95 inference latency by 30%.”