Senior Research Engineer

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. 

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 Senior Research Engineer, you will: 

• Bridge the gap between state-of-the-art research and customer-facing features 

• Drive systems-level innovation across models, infrastructure, and deployment 

• Champion responsible AI by embedding fairness, safety, privacy, and performance from the ground up 

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%.” 

Specialty Technical Areas 

• Large-scale training and fine-tuning of LLMs, vision-language, or multimodal models 

• Multi-agent systems, dialogue agents, and copilots 

• Optimization of inference speed, accuracy, reliability, and cost in production 

• Retrieval systems and hybrid architectures using RAG and vector databases 

• ML for real-world data constraints such as missing data, noisy labels, and class imbalance