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Microsoft

Applied Scientist II

Posted 2 Days Ago
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In-Office
Redmond, WA
102K-219K Annually
Junior
In-Office
Redmond, WA
102K-219K Annually
Junior
Develop and deploy large-scale ML and LLM solutions to improve Bing Search relevance and engagement. Build neural ranking models, feature processing frameworks, use reinforcement learning and user feedback, and scale production ML systems for millions of daily search experiences.
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Overview

Our team is leveraging cutting-edge machine learning and LLM technologies to improve user engagement in Bing Search. We are tackling exciting challenges such as:

  • Applying LLMs to improve search relevance and automate training data generation.
  • Building state-of-the-art large-scale neural ranking models and feature processing frameworks.
  • Leveraging reinforcement learning and user feedback signals to optimize long-term user engagement and retention.
  • Developing scalable ML systems that power millions of search experiences every day.

If you're passionate about pushing the boundaries of search and AI, we'd love to hear from you. Feel free to apply below or message me if you have any questions!

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.


Responsibilities
  • Applying LLMs to improve search relevance and automate training data generation.
  • Building state-of-the-art large-scale neural ranking models and feature processing frameworks.
  • Leveraging reinforcement learning and user feedback signals to optimize long-term user engagement and retention.
  • Developing scalable ML systems that power millions of search experiences every day.

Qualifications

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.

Preferred Qualifications:

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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 background in machine learning, deep learning, and large-scale AI systems.
  • Experience with LLMs, transformer-based models, retrieval, ranking, or recommendation systems.
  • Solid understanding of neural network architectures, feature engineering, and model optimization.
  • Experience developing and deploying production-scale ML systems.
  • Proficiency in Python and familiarity with modern ML frameworks such as PyTorch or TensorFlow.
  • Solid software engineering skills, including distributed systems, data processing, and system design.
  • Experience with reinforcement learning, online experimentation (A/B testing), or user engagement optimization is a plus.
  • Excellent problem-solving, communication, and collaboration skills.

#MicrosoftAI 


Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 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 $133,800 - $219,200 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.

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