Principal Applied Scientist

Copilot Discover helps hundreds of millions of people be informed, entertained, and inspired by surfacing highly relevant, trustworthy, and delightful content across Microsoft surfaces. We’re building the next generation of AI‑powered content recommendation systems—spanning text, images, audio, and video—to curate the right content at the right moment for each user while upholding safety and integrity.

As a Principal Applied Scientist, you’ll lead the science behind Discover’s ranking, user understanding, and content understanding stack, combining LLMs, multimodal models, and large‑scale recommender systems to drive measurable gains in engagement, satisfaction, and trust. You will set technical direction, mentor a high‑caliber science cohort, and partner closely with engineering, PM, UXR, and policy to ship end‑to‑end outcomes. You will contribute to the development of the next generation of MSN that is adopting the latest generative AI techniques.

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.

• Advance the recommendation & ranking stack. Architect and productionize large‑scale DNN/LLM‑enhanced recommenders (representation learning, sequence modeling, retrieval/ranking, slate optimization), balancing user satisfaction, content quality, and business goals.
• Deepen user & content understanding. Gather and analyze user signals from diverse sources to gain a thorough understanding of user behaviors and utilize ML/AI techniques to interpret and predict user needs and preferences. Design and build models that assess content quality and utility aspects to ensure product safety and drive sustainable user engagement.
• Scale E2E ML/AI systems. Collaborate with engineering on data contracts, feature stores, distributed training/inference, and automated rollout/rollback; drive architectural investments that increase agility and reliability of Discover’s AI platform.
• Drive innovation in AI-forward products. Collaborate with product, science and engineering team closely to innovate in AI-forward products, including agentic content feed experience with hyper personalized AI-generated content and generative UI.
• Mentor & influence. Provide technical leadership across problem framing, methodology selection, code quality, and publishing/knowledge‑sharing; uplevel peers through design reviews, deep‑dives, and principled decision‑
• Stay close to users. Translate user engagements and behavioral history into model objectives and product bets; ensure our AI solutions elevate relevance, transparency, and engagement for real users.

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ 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 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 5+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.

Preferred Qualifications:

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 6+ years of industry experience on applied AI/ML for web scale products.
• Having publications at top AI/ML conferences (e.g., KDD, SIGIR, NIPS, ICML, ICLR, RecSys, ACL, CIKM, CVPR, ICCV, etc.).
• Demonstrated capability to grow the business through the innovation of ML algorithms.
• Experience in Software Engineering and familiar with ML Infra.

#MicrosoftAI #mai #recsys #copilot

Applied Sciences IC6 - The typical base pay range for this role across the U.S. is USD $165,600.00 - $296,400.00 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 $220,800.00 - $331,200.00 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.