Senior Applied Scientist

Core Search and AI team (Bing) is looking for people who want to build the next generation of search using advanced AI technologies, especially large language models, at scale. We are responsible for the largest machine learning models at Microsoft by volume and take pride in being the first in the world to solve many practical AI at Scale challenges. Our work spans a very large scope of scenarios to deliver high quality search results by document/answers summarization for representation and ranking, query and document understanding, and AI search grounding.

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.

• Design, train, and improve large-scale machine learning models for Bing Search relevance and ranking, with a focus on transformer-based and LLM-powered approaches.
• Own end-to-end model development, including problem formulation, feature design, training, offline evaluation, and online A/B experimentation.
• Develop and optimize multi-stage ranking stacks (recall, coarse ranking, fine ranking) to deliver high-quality, low-latency search results at global scale.
• Leverage LLMs for query and document understanding, summarization, representation learning, and grounding to enhance ranking quality.
• Address cold-start and sparse-data challenges through content understanding, pre-training, and representation sharing.

Required Qualifications:

• 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.

Additional or preferred qualifications :

#MicrosoftAI 

Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

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.