Overview

The Core Recommendation Ranking team in Microsoft AI Content Org powers the end-to-end ranking and reranking stack behind Microsoft's content experiences — including news, interest, video, and AI-generated content (AIGC) feeds, reaching hundreds of millions of users worldwide. We are at the forefront of integrating Generative AI and agentic systems into large-scale recommendation pipelines. We are seeking a Senior Applied Scientist to design, build, and optimize ranking and recommendation models that directly impact user engagement across Microsoft's content ecosystem. In this role, you will work hands-on with cutting-edge deep learning and LLM-enhanced ranking systems while collaborating closely with engineering and product partners to deliver production-quality solutions at scale.

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

• Design & implement ranking, reranking, and retrieval models using deep learning, LLMs, and advanced recommendation techniques.
• Own end-to-end ML pipelines — feature engineering, model training, offline/online evaluation, and production inference optimization.
• Innovate by applying state-of-the-art methods including LLM-enhanced ranking, contextual bandits, reinforcement learning, and generative recommendation approaches.
• Collaborate cross-functionally with engineering, product, and platform teams to translate research insights into shipped features.
• Contribute to technical direction within the team — propose experiments, identify opportunities, and drive projects from ideation to production.
• Mentor less experienced scientists and engineers, fostering a culture of technical excellence and knowledge sharing.

Qualifications 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.

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.
• 4+ years of industry experience in applied science, machine learning, or deep learning at scale.
• Solid foundation in recommendation systems, ranking models, or search relevance.
• Hands-on experience with deep learning frameworks (Py Torch or Tensor Flow) and cloud-scale ML infrastructure.
• Proficiency in Python and data processing tools (Spark, Pandas, or equivalent).
• Track record of shipping ML models to production with measurable user impact.
• Experience with LLM-based ranking, retrieval-augmented generation (RAG), or generative recommendation systems.
• Familiarity with multi-objective optimization, heterogeneous signal fusion, or user modeling.
• Experience with online experimentation (A/B testing, interleaving) and metrics-driven development.
• Publications at top venues (NeurIPS, ICML, KDD, WWW, Rec Sys, SIGIR).
• Exposure to agentic AI systems or autonomous content curation pipelines.
• Experience with distributed ML training and large-scale data pipelines.

#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 $158,400 - $258,000 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.