Applied Sciences IC2

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Are you passionate about building intelligent systems that enhance productivity in Office applications? Do you have a strong foundation in machine learning, natural language processing (NLP), and software engineering? Join our team of Applied Scientists and engineers working at the forefront of generative AI to shape the future of productivity tools.

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 Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
  • OR equivalent experience.
• Minimun 1 year of industry experience in applied machine learning or data science roles.
• Proficiency in Python
• Experience developing and evaluating generative AI models.
• Familiarity with NLP techniques and content understanding.
• Strong verbal and written communication skills to explain complex technical concepts to diverse audiences.
• Ability to tackle open-ended problems and deliver practical, scalable solutions.

Preferred Qualifications:

• Proficiency in ML libraries such as PyTorch or TensorFlow.
• Experience deploying ML models in production environments.
• Exposure to full product lifecycle from prototyping to deployment and iteration.
• Contributions to research publications, patents, or open-source projects.

• Applied Research & Development: Contribute to the design and implementation of machine learning models and algorithms for search, summarization, and content understanding in Office applications.
• Model Development: Build and fine-tune ML/DL models using frameworks like PyTorch or TensorFlow. Collaborate on deploying models in production environments with a focus on scalability and performance.
• Cross-Functional Collaboration: Work closely with engineering, product, and research teams to translate business needs into technical solutions and deliver impactful features.
• Experimentation & Evaluation: Conduct experiments, analyze results, and iterate on solutions to improve precision, recall, and user satisfaction.
• Innovation: Stay current with research trends in generative AI and NLP. Explore new signals, data sources, and modeling techniques to evolve intelligent systems and contribute to the Copilot ecosystem.