Role overview
Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers to levels they cannot achieve anywhere else. This is a world of more possibilities, more innovation, and more openness in a cloud-enabled world.
It is an incredible time for AI initiatives at Microsoft. We're pushing the boundaries of AI and Machine Learning across multiple domains to advance Microsoft’s ambitious AI goals. Microsoft Copilot Studio is our comprehensive agent building platform that lets its customers create conversational and autonomous agents using natural language or a graphical interface. Copilot Studio makes it simple to design, test, and deploy solutions tailored to its customers’ industry, company, department, or role, whether they need agents for internal or external use cases.
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.
This role will combine machine learning and AI knowledge with software engineering expertise, while demonstrating a growth mindset and customer empathy. Join us in shaping the future of AI agents.
You will play a crucial role in developing the Copilot Studio Applied Science and Research team’s direction in machine learning, Generative AI model fine-tuning, Agent creation and deployment, AI evaluation and scaling.
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.
Responsibilities
- 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.
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
- OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ year(s) related experience (e.g., statistics, predictive analytics, research)
- OR equivalent experience.
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred qualifications
- Bachelor’s degree in Computer Science, Statistics, Electrical/Computer Engineering, Physics, Mathematics or related field AND 9+ years of experience in AI/ML, predictive analytics, software engineering or research OR Master’s degree AND 7+ years of relevant experience OR PhD AND 5+ year of relevant experience OR equivalent experience
- OR Master’s degree AND 7+ years of relevant experience
- OR PhD AND 5+ year of relevant experience
- OR equivalent experience
- Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines.
- Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow).
- Familiar with AI coding assistant, AI IDE, Agentic coding assistant such as GitHub Copilot, and Cursor.
- 3+ years of experience conducting applied AI or NLP research in academic or industry settings.
- 3+ year of experience developing and deploying live production systems or AI services.
- 2+ years of experience working with Generative AI or large-scale deep learning models and ML stacks.
- Experience across the product lifecycle from ideation to shipping.