Applied Scientist II

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

The Applied Scientist II will design, develop, and ship AI models into the Teams media stack, focusing on real-time conversation products like Teams. This role involves building end-to-end ML systems, from data cleaning and feature engineering to model training, evaluation, and deployment, with an emphasis on optimizing for performance and memory, and updating deployed models based on A/B testing.

What you'd actually do

  1. Collaborating with AI researchers and audio signal processing experts to design and build end-to-end machine learning (ML) systems, tuned for human-human and human-AI interactions.
  2. Design and develop machine learning (ML) pipelines involving data cleaning, feature engineering, model training, and evaluation.
  3. Work across the product lifecycle from prototyping to shipping production-grade code optimized for performance and memory and updating the deployed models based on A/B testing.
  4. Remain up to date with latest advancements, trends and research and contribute towards our IP portfolio.

Skills

Required

  • development/deployment of machine learning algorithms
  • 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

  • 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
  • Experience explaining complex ideas to technical and non-technical audiences.
  • Experience contributing code to production systems or shipped products.
  • Experience building and maintaining production-grade ML (machine learning) pipelines.

What the JD emphasized

  • shipping production-grade code
  • updating the deployed models based on A/B testing
  • machine learning (ML) pipelines
  • design and build end-to-end machine learning (ML) systems

Other signals

  • shipping AI models
  • production-grade code
  • ML pipelines
  • A/B testing