Applied Scientist - AI Experiences Search

Microsoft•Redmond, WA

1d•$102,100 - $219,200

About The Position

The Microsoft AI Web Data team is seeking an Applied Scientist to help build the next-generation platform for Bing and Microsoft AI. The Microsoft AIX Web Data Platform (WDP) team builds the data foundation that powers Bing and Microsoft AI experiences, including large-scale grounding and large language model (LLM) training. It operates end-to-end systems that discover, crawl, process, understand, and store web content at internet scale, advancing platform capabilities with state-of-the-art modeling. In this role, you will translate research into production by advancing the state of the art and applying it to current AI needs. You will drive the design, development, execution, and implementation of research projects, using scientific principles and techniques to develop, evaluate, and deploy algorithms and solutions that improve system performance, quality, data management, and accuracy. The work aims to shape AI experiences and deliver value to millions of users.

Requirements

Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 2+ years of related experience, such as statistics, predictive analytics, or research; OR a master's degree in one of these fields and 1+ year of related experience; OR a doctorate in one of these fields; OR equivalent experience.

Nice To Haves

1+ years of experience contributing to research publications, patents, or peer-reviewed academic papers.
1+ years of experience in product development in machine learning and related areas.
Hands-on experience developing algorithms and models using deep learning frameworks such as TensorFlow and PyTorch.
Hands-on experience with large language models (LLMs), such as GPT, BERT, or similar architectures, and understanding of their capabilities and limitations.
Knowledge of natural language processing (NLP), including tokenization, semantic understanding, representation learning, classification, and text generation.
Understanding of modern machine learning and deep learning techniques, with hands-on experience using architectures such as Transformers/Attention, DNNs, CNNs, or RNNs and frameworks such as PyTorch, TensorFlow, or Keras.
Algorithmic, statistical, and analytical skills, with demonstrated ability to apply advanced techniques to complex, real-world problems.
Ability to translate technical vision into experimentation, system design, implementation, and measurable results, working independently and collaboratively across teams.
Experience with large-scale or distributed data processing, high-performance computing, stream processing, or SCOPE.

Responsibilities

Own end-to-end applied research and engineering to improve web data quality, scale, and model readiness for Search, grounding, and LLM training.
Develop and apply state-of-the-art machine learning, LLM, statistical, and data mining techniques to solve complex web-scale problems and advance platform capabilities.
Analyze large-scale web data, formulate hypotheses, and design rigorous experiments that produce trustworthy, actionable insights and measurable impact.
Apply LLMs and prompt engineering to score, classify, label, and evaluate data, enabling scalable model training, evaluation, and continuous improvement.
Translate complex technical problems and research findings into product decisions, technical strategies, and engineering roadmaps in partnership with stakeholders.
Collaborate across Microsoft AI, document and share learnings, and establish best practices for scalable, responsible, and privacy-conscious AI and data solutions.