Job Description

Are you an

Applied Scientist II

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.

Responsibilities

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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.

Qualifications

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Required Qualifications:

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
+ OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
+ OR equivalent experience.
2+ years of industry experience in applied machine learning or data science roles. Proficiency in Python and ML libraries such as PyTorch or TensorFlow. Experience developing and evaluating generative AI models. Familiarity with NLP techniques and content understanding.

Other Requirements:

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

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:

Solid verbal and written communication skills to explain complex technical concepts to diverse audiences. Ability to tackle open-ended problems and deliver practical, scalable solutions. 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.

Microsoft is an equal opportunity employer. Consistent with applicable law, 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 and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.