# Senior Applied Scientist

**Company**: Microsoft AI
**Location**: Redmond
**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-52/
**Canonical**: https://yubhub.co/jobs/job_b071398a-057

## 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.

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:

- 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.

- 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 (PyTorch or TensorFlow) 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, RecSys, SIGIR).

- Exposure to agentic AI systems or autonomous content curation pipelines.

- Experience with distributed ML training and large-scale data pipelines.

## Skills

### Required
- deep learning
- LLMs
- advanced recommendation techniques
- feature engineering
- model training
- offline/online evaluation
- production inference optimization
- state-of-the-art methods
- contextual bandits
- reinforcement learning
- generative recommendation approaches
- Python
- data processing tools
- Spark
- Pandas
- PyTorch
- TensorFlow
- cloud-scale ML infrastructure
- recommendation systems
- ranking models
- search relevance
- multi-objective optimization
- heterogeneous signal fusion
- user modeling
- online experimentation
- metrics-driven development
- agentic AI systems
- autonomous content curation pipelines
- distributed ML training
- large-scale data pipelines