Applied Scientist II

Microsoft Microsoft · Big Tech · Redmond, WA +2 · Applied Sciences

Applied Scientist II at Microsoft AI focusing on Generative AI and Agentic Modeling for consumer products like Bing and Copilot. The role involves building and optimizing production ML models, working with SOTA generative models, analyzing large-scale data, designing experiments, and delivering insights for business decisions. Requires expertise in ML, Generative AI, Agentic Modeling, or Data Science, with hands-on experience with LLMs/SLMs.

What you'd actually do

  1. Building and owning production machine learning models to improve results.
  2. Working hands on with SOTA generative models like Qwen, Llama, Mistral, GPT and others, to deliver big impact.
  3. Finding insights and forming hypothesis on large-scale data with various machine learning, feature engineering, statistical, and data mining techniques: e.g. regression, classification, NLP, optimization.
  4. Designing experiments, understanding the resulting data, and producing actionable, trustworthy conclusions from them.
  5. Wrangling large amounts of data (think petabytes) using various tools, including open-source ones and your own.

Skills

Required

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience OR Master's Degree AND 1+ year(s) related experience OR Doctorate OR equivalent experience
  • Ability to meet Microsoft, customer and/or government security screening requirements
  • Microsoft Cloud Background Check

Nice to have

  • 5+ years related experience with Bachelor's Degree OR 3+ years related experience with Master's Degree OR 1+ year(s) related experience with Doctorate
  • 2+ years of proficient experience with C#/C++/Java/Python/Scala or any other OOP skills with a good knowledge of Data Structures/Algorithms
  • Passionate and have experience in data analytics, applied machine learning, deep learning and/or related fields.

What the JD emphasized

  • production machine learning models
  • large-scale data
  • petabytes

Other signals

  • Generative AI
  • LLMs
  • Agentic Modeling
  • Large-scale modeling optimization
  • Production ML models