Overview
We are building the next generation of AI-powered recommendation and generative experience systems across Microsoft’s consumer ecosystem, including Edge, Windows, Mobile, Copilot, and Microsoft Start Network (MSN). Our team owns the end-to-end personalization stack, spanning large-scale retrieval, deep ranking, whole-page optimization, reinforcement learning, and LLM(Large Language Model)-powered generative experiences.
We develop intelligent, context-aware systems that optimize engagement, quality, trust, and monetization on a global scale, serving billions of requests daily with low-latency production infrastructure.
You will work on cutting-edge challenges in recommendation systems, personalization, and generative AI, including:
- Large-scale retrieval, ranking, and reranking systems.
- Deep learning models for engagement and long-term user satisfaction.
- Sequential and context-aware recommendation using Transformers, DLRM, MMoE, and multi-task learning.
- Whole-page optimization across content, ads, layout, and user experience.
- Multi-objective optimization balancing engagement, quality, and revenue.
- LLM-powered generative and agentic experiences.
- Multi-agent systems for retrieval, grounding, summarization, and content generation.
- Real-time personalization, experimentation, and online learning systems.
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
In this role you will:
- Lead the design and development of large-scale recommendation, ranking, personalization, and generative AI systems serving billions of users across Microsoft consumer products.
- Drive technical strategy and architecture across retrieval, ranking, reranking, whole-page optimization, and LLM-powered experiences.
- Develop state-of-the-art machine learning models for engagement optimization, long-term user satisfaction, monetization, and trust-aware personalization.
- Lead innovation in generative AI, multi-agent systems, reinforcement learning, and context-aware recommendation technologies.
- Define and drive multi-objective optimization frameworks balancing user engagement, quality, diversity, trust, and revenue outcomes.
- Partner cross-functionally with engineering, product, design, infrastructure, and business teams to deliver high-impact AI-powered user experiences.
- Lead large-scale experimentation efforts, establish success in metrics and guardrails, and make data-driven product and modeling decisions.
- Drive end-to-end execution from research and prototyping to production deployment and long-term platform evolution.
- Stay at the forefront of advances in machine learning, recommendation systems, distributed systems, and generative AI, and translate emerging technologies into scalable production impact.
Qualifications
**Required Qualifications: **
Bachelor's Degree in 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 Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research).
- OR Doctorate in 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 or Doctorate in Computer Science, Machine Learning, Statistics, or related field.
Proven experience in machine learning (ML), recommender systems, ranking, search, or personalization.
Experience building and shipping large-scale production ML systems.
Experience with deep learning frameworks such as PyTorch or TensorFlow.
Understanding of experimentation, metrics, and model evaluation.
Experience with billion-scale user systems and low-latency serving.
Publication or innovation track record in ML/AI systems.
Demonstrated technical leadership and cross-functional influence.
Experience with any of the below:
- Recommendation systems.
- Ranking/search systems.
- Ads optimization.
- Sequential modeling.
- Reinforcement learning.
- LLMs and generative AI.
- Multi-agent systems.
- Large-scale distributed systems.
#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.**