Applied Scientist - AI Experiences Search

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. We operate end-to-end systems that discover, crawl, process, understand and store web content on the internet scale. We advance the platform’s capabilities with state-of-the-art modeling and fueling critical Microsoft experiences and pushing the frontier of AI.

This is a great opportunity for someone who enjoys tackling deep technical challenges and driving industry-wide impact. In this role, you will translate research into production by advancing the state of the art and applying it to meet today’s 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.

Join us to shape the future of AI and deliver meaningful value to millions of users.

At Microsoft, our mission-to empower every person and every organization on the planet to achieve more-guides how we partner with customers to deliver trusted, impactful solutions. With a growth mindset culture, we innovate responsibly and measure success by shared progress-people, teams, and customers. Join us to do meaningful work that changes the world and helps shape what’s next for everyone.

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 roadmap 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.

• Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
• OR equivalent experience., * 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 PyTorchHands-on experience with large language models (LLMs), such as GPT, BERT, or similar architectures, with an 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 both independently and collaboratively across teams.
• Experience with large-scale or distributed data processing, high-performance computing, stream processing, or SCOPE is preferred.