# Senior Applied Scientist

**Company**: Microsoft AI
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: $119,800 - $234,700 per year
**Category**: Engineering
**Industry**: Technology

**Apply**: https://microsoft.ai/job/senior-applied-scientist-54/
**Canonical**: https://yubhub.co/jobs/job_05eb0a81-c0c

## Description

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.

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.

## Skills

### Required
- deep learning
- LLMs
- advanced recommendation techniques
- feature engineering
- model training
- offline/online evaluation
- production inference optimization
- state-of-the-art methods
- LLM-enhanced ranking
- contextual bandits
- reinforcement learning
- generative recommendation approaches

### Nice to have
- distributed ML training
- large-scale data pipelines
- agentic AI systems
- autonomous content curation pipelines