Are you interested in building personalized recommendation for billions of users, especially in finance domains? Finance Recommendation team in Content Service organization is building personalized recommendation in finance domains in various product, including Copilot, MSN and Edge default home page, etc. Our team focus on whole recommendation stack building, especially the modeling parts in different recommendation layers, including document understanding, segment recall, user profile modeling, personalized ranking, diversity optimization, etc. If you’re looking for one team to utilize your LLM skills to optimize user engagement of real product, grow your LLM and modeling skills by iterating against users’ feedbacks and resolving real product challenges, then this is the team for you!

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.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

• Introduce state-of-the-art research and technology into products by designing scalable, data-driven solutions and leading technology transfer initiatives.
• Analyze product scenarios and objectives to identify key challenges, design experimental processes for iteration and optimization, and collaborate with platform teams to ensure efficient online deployment and performance improvements.
• Work both independently and with research and product teams to apply advanced methods, negotiate practical solutions, and stay current with trends in personalized recommendation, deep learning, and AI.
• Mentor and support junior team members, assist with recruitment, and build strong connections with academia to attract top research talent and encourage interdisciplinary collaboration.
• Use expertise in areas such as Machine Learning, Natural Language Processing, and Computer Vision to develop algorithms, manage data pipelines, and apply statistical analysis to enhance product quality, scalability, and performance.

• Bachelor'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 Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate 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 equivalent experience.
• Solid coding skill and good experience on deep learning framework like PyTorch, TensorFlow, CNTK, etc.

Preferred Qualifications:

• Master'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 Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• Working / research experiences on recommendation areas.
• Good communication skills and self-motivated.

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.