The Microsoft Copilot Studio Applied Science and Research organization seeks a seasoned Principal Applied Scientist, a machine learning engineer to contribute to the development and integration of cutting-edge AI technologies into Microsoft Copilot Studio, ensuring they are inclusive, ethical, and impactful. You will collaborate across product, design, research and engineering teams to bring innovative solutions to life, applying your expertise in machine learning, data science, and software engineering to solve complex problems. Your work will directly influence product quality and customer experiences.

Design, implement and experiment with AI architectures for improving Large Language Models (LLM) inference systems. This includes design patterns pertaining, but not limited to, Retrieval Agumented Generation (RAG), Context Engineering, Multi-Agent architectures, tool design etc.

Develop robust evaluation frameworks to assess model performance, monitor model behavior, conduct systematic benchmarking, and address identified weaknesses while ensuring compliance with customer standards.

Build and maintain internal tools to streamline model fine-tuning and evaluation workflows and automate repetitive tasks within secure development environments.

Prepare and analyze data for machine learning, identifying optimal features and addressing data gaps.

Implement machine learning algorithms, large-scale model fine-tuning, especially with closed and open source LLMs, Small Language Models (SLMs), multimodal or task-specific models to solve real-world customer problems and deliver measurable product and customer impact.

Contribute to or enhance existing innovations by continuously refining well-established models and training techniques through iterative improvements.

Write efficient, high quality production code and debug complex distributed systems.

Provide deep subject matter expertise in AI subfields (e.g., deep learning, Generative AI, Natural Language Processing(NLP), muti-modal models, reinforcement learning) to help translate cutting-edge research into practical, real-world solutions that drive product innovation and business impact.

Demonstrate deep understanding of small and large language models (SLMs and LLMs) architecture and optimization techniques to adapt out-of-the-box solutions to particular business problems.