Applied Scientist - AI Experiences Search

Quick Facts

• Focus: web-scale data quality, Search/grounding, and LLM training readiness
• Scope: end-to-end applied research and engineering to production

Description

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, including prompt-based workflows to score, classify, label, and evaluate data. Translate research into product decisions and measurable system improvements while collaborating on responsible, privacy-conscious AI and data solutions.

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 ML, LLM, statistical, and data mining techniques for web-scale problems
• Analyze large-scale web data, define hypotheses, and design rigorous experiments
• Use LLMs and prompt engineering to score, classify, label, and evaluate data for scalable training and continuous improvement
• Translate research findings into technical strategies and engineering roadmaps with stakeholders
• Collaborate across teams, document learnings, and establish best practices for scalable, responsible, privacy-conscious AI/data solutions

Requirements

• Bachelor's degree in a related field with 2+ years related experience, OR Master's degree with 1+ year related experience, OR Doctorate in a related field, OR equivalent experience
• Experience in statistics, predictive analytics, or research
• Hands-on deep learning development using TensorFlow or PyTorch
• Hands-on experience with LLMs (e.g., GPT or BERT) and understanding their capabilities and limitations

Benefits

• Eligible for benefits and other compensation (role-dependent)
• Pay range information available via Microsoft corporate pay page