Applied Scientist II

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

Applied Scientist II at Microsoft working on improving user engagement in Bing Search by applying LLMs for search relevance and training data generation, building large-scale neural ranking models, and leveraging reinforcement learning. The role involves developing scalable ML systems for millions of daily search experiences.

What you'd actually do

  1. Applying LLMs to improve search relevance and automate training data generation.
  2. Building state-of-the-art large-scale neural ranking models and feature processing frameworks.
  3. Leveraging reinforcement learning and user feedback signals to optimize long-term user engagement and retention.
  4. Developing scalable ML systems that power millions of search experiences every day.

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 in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

Nice to have

  • Solid background in machine learning, deep learning, and large-scale AI systems.
  • Experience with LLMs, transformer-based models, retrieval, ranking, or recommendation systems.
  • Solid understanding of neural network architectures, feature engineering, and model optimization.
  • Experience developing and deploying production-scale ML systems.
  • Proficiency in Python and familiarity with modern ML frameworks such as PyTorch or TensorFlow.
  • Solid software engineering skills, including distributed systems, data processing, and system design.
  • Experience with reinforcement learning, online experimentation (A/B testing), or user engagement optimization is a plus.
  • Excellent problem-solving, communication, and collaboration skills.

What the JD emphasized

  • large-scale neural ranking models
  • reinforcement learning
  • scalable ML systems

Other signals

  • LLMs
  • neural ranking models
  • reinforcement learning
  • ML systems