Applied Scientist II

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Microsoft Teams is the hub for teamwork that integrates all the people, content, and tools your team needs to be more engaged and effective. It is core to Microsoft’s modern work, modern life & modern education value prop. We are reinventing the way people communicate and work together across the globe. We own the infrastructure that enables complex orchestration of Copilot workflows to put powerful AI capabilities at the user’s fingertips.

AI is now going through a transformational change with the advent for LLMs, we need an individual who has expertise in working with Large Language models (LLMs) who has designed scalable systems using LLMs. As an Applied Scientist II you will need to design and build systems that allow LLMs to reason over large amounts of data as well as leveraging lighter weight models in place for specific scenarios for specific scenarios. We are looking for an individual who has the proven capability of working with research teams and partnering with them to deliver joint solutions. This can range from working together to build fine-tuned models to coming up with ways to build custom LLMs for specific product needs.

We are excited to hear from candidates who are passionate about making a significant impact on how people interact with their computers in the last 30 years, and who are excited about the opportunity to be at the forefront of growing new business for Microsoft. This is a rare chance to be part of a cutting-edge technology that is poised to revolutionize productivity and innovation.

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.

Required

• Educational Background: Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Electrical Engineering, or other related technical field (e.g., mathematics, physics, statistics, or similar).
• Experience: At least 3+ years of industry experience, with strong proficiency in coding in Python for software development and data science. (Experience with additional programming languages is a plus but not required.)
• Skills: Proficiency in Python-based machine learning frameworks and libraries such as PyTorch and TensorFlow, and experience developing and deploying generative AI or machine learning models into production. Strong communication and presentation skills, with the ability to convey complex technical concepts to diverse audiences.
• Problem-Solving: Demonstrated ability and motivation to understand complex business contexts and to solve hard, open-ended problems. A proactive mindset in tackling challenges and optimizing solutions to deliver business impact.
• Additional or Preferred Qualifications
• Advanced Degree (Preferred): Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field and 3+ years of related experience; OR Doctorate in one of the above fields and 6+ years related experience; OR equivalent industry experience.
• Research & Publications: 3+ years of experience contributing to research publications (e.g., patents, academic papers, open-source libraries) that showcase innovative thinking and technical depth.
• Research Excellence: 3+ years of experience conducting original research as part of a formal research program (in academic or industrial settings), with proven ability to drive insights from concept to execution.
• Production Deployment: 3+ years of experience developing and deploying live production systems as part of a product team, ensuring solutions are scalable, reliable, and maintainable.
• Product Lifecycle Experience: 3+ years of experience contributing to products or systems at multiple stages of the development lifecycle, from initial ideation and prototyping to final production deployment and ongoing iteration.

• Conduct applied science experiments, create and validate metrics, develop ML pipeline and modeling algorithm in the area of Large Language Models, Natural Language Processing, Information Retrieval, and Machine Learning.
• Develop and deploy conversational and language understanding models at scale.
• Follow and advance best practices for Responsible AI and Privacy Preserving Machine Learning.
• Collaborate closely with Microsoft Research, Microsoft AI groups, Microsoft Azure, AI platform teams, and product teams to create the next generation of AI innovation in our products and services.
• Embody our culture and values