Applied Scientist

Job Description 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, more openness in a cloud-enabled world.The Business & Industry Copilots group is a rapidly growing organization that is responsible for the Microsoft Dynamics 365 suite of products, Power Apps, Power Automate, Dataverse, AI Builder, Microsoft Industry Solution and more. Microsoft is considered one of the leaders in Software as a Service in the world of business applications and this organization is at the heart of how business applications are designed and delivered. We are looking for a Applied Scientist to join our team This is an exciting time to join our group and work on something highly strategic to Microsoft in shaping the future of the autonomous enterprise and lead the development of intelligent, agent-first experiences that transform how businesses operate. The Business and Industry Solutions (BIS) team is looking for a Senior Applied Scientist to drive innovation at the intersection of AI, experimentation, and enterprise systems. In this role, you will design and evaluate autonomous agents that deliver measurable improvements in accuracy, latency, and cost-efficiency. You'll lead rapid experimentation cycles, develop robust evaluation frameworks, and apply advanced techniques like reinforcement learning to enable multi-step reasoning and decision-making. You'll collaborate across engineering, product, and partner teams to ensure agents are performant, secure, reliable, and extensibleempowering customers and partners to build on our platform. This is your opportunity to influence the next generation of AI-native business applications and deliver real-world impact at scale. The ideal candidate has prior expertise in natural language processing (NLP), with a strong foundation in large language model (LLM) development, evaluation, and fine-tuning. Desirable to have hands-on experience in applying advanced fine-tuning techniquesincluding instruction tuning, reinforcement learning from human feedback (RLHF), and tool-augmented generationto build agents capable of multi-step reasoning and decision-making. Familiarity with prompt/context engineering, context-aware orchestration, and integrating LLMs with external tools and APIs is essential. The candidate should be comfortable working in a fast-paced, experimentation-driven environment, leveraging both offline and online evaluation methods to iterate rapidly and optimize agent behavior. A deep understanding of the challenges and opportunities in building AI-native enterprise applications will be key to success in this role. 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 - Deliver impactful solutions by executing highleverage data science and analytics initiatives within a product area or feature team, ensuring measurable improvements to user and business outcomes. - Lead the design and implementation of advanced model finetuning pipelines, including Reinforcement Learning from Human Feedback (RLHF), to align AI system behavior with user intent and improve performance in realworld scenarios. - Own complex, endtoend projects that combine technical depth with crossfunctional collaboration, influencing feature direction and prioritization rather than broad organizational investment decisions. - Foster alignment and trust across partner teams through clear, actionable communication and collaborative problemsolving. - Develop and maintain robust measurement systems, experimentation frameworks, and causal inference methodologies tailored to dynamic AI systems and enterprisescale environments. - Mentor and support peers by sharing best practices, reviewing designs, and contributing to a collaborative, highperformance team culture. - Leverage AI to streamline workflows and enhance team productivity through intelligent automation and innovation. Qualifications The ideal candidate has prior expertise in natural language processing (NLP), with a strong foundation in large language model (LLM) development, evaluation, and fine-tuning. Desirable to have hands-on experience in applying advanced fine-tuning techniquesincluding instruction tuning, reinforcement learning from human feedback (RLHF), and tool-augmented generationto build agents capable of multi-step reasoning and decision-making. Familiarity with prompt/context engineering, context-aware orchestration, and integrating LLMs with external tools and APIs is essential. The candidate should be comfortable working in a fast-paced, experimentation-driven environment, leveraging both offline and online evaluation methods to iterate rapidly and optimize agent behavior. A deep understanding of the challenges and opportunities in building AI-native enterprise applications will be key to success in this role. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.