Senior Applied Scientist

Microsoft • Redmond, WA

21h • Hybrid

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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 (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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees