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.