Applied Scientist II

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Applied Sciences

Applied Scientist II on the Core Search and AI team (Bing) to build the next generation of search using large language models at scale. Responsibilities include developing retrieval, ranking, and relevance systems, refining RAG pipelines, and translating research into production deployments. Requires experience with ML systems for search/ads/recommendation and LLMs.

What you'd actually do

  1. develop next‑generation search capabilities by building and optimizing retrieval, ranking, and relevance systems that integrate deeply with LLM‑powered experiences.
  2. refine RAG pipelines that enhance retrieval fidelity, reduce hallucinations, and deliver more context‑aware, user‑aligned responses in production environments.
  3. translate research into production by running experiments, analyzing results, and collaborating with engineering partners to deploy scalable, reliable model improvements.
  4. monitor and evaluate model performance using quantitative metrics, qualitative assessments, and user‑centric evaluation frameworks to ensure continuous improvement.

Skills

Required

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience
  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience
  • Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
  • equivalent experience
  • coding in Python, C++, C#, C or Java
  • industry experience applying Machine Learning techniques

Nice to have

  • 2+ years of experience coding in Python, C++, C#, C or Java
  • 1+ year of industry experience applying Machine Learning techniques
  • Experience building and improving large scale Machine Learning system for search, ads, and recommendation, adopting LLM.
  • Research background on Machine Learning, LLM and NLP.
  • Proficient problem solver: ability to identify and solve problems that the world has not solved before.

What the JD emphasized

  • build the next generation of search using advanced AI technologies, especially large language models, at scale
  • largest machine learning models at Microsoft by volume
  • first in the world to solve many practical AI at Scale challenges
  • improve the relevance for the next generation of search
  • Experience building and improving large scale Machine Learning system for search, ads, and recommendation, adopting LLM.
  • Proficient problem solver: ability to identify and solve problems that the world has not solved before.

Other signals

  • large scale machine learning models
  • LLM-powered experiences
  • RAG pipelines
  • retrieval and reranking model for search results optimization