Principal Applied Scientist

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

Principal Applied Scientist to drive the science vision, technical strategy, and execution for relevance, intent understanding, personalization, recommendation, and agent-driven commerce experiences across Search, Shopping, Copilot, and emerging agentic surfaces. This role involves deep hands-on machine learning innovation and technical leadership for large-scale production deployment.

What you'd actually do

  1. Serve as the technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives across Copilot, Shopping, and Ads experiences.
  2. Drive innovation in machine learning technologies including LLMs, SLMs, multimodal AI, retrieval, ranking, personalization, and recommendation systems.
  3. Define and execute the science roadmap for user intent understanding, product understanding, content relevance, and advertiser matching.
  4. Lead end-to-end ML development, including model architecture, training data strategy, evaluation, experimentation, calibration, and production deployment.
  5. Partner with engineering and product teams to deliver scalable, reliable, and cost-efficient AI systems.

Skills

Required

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience
  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience
  • Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience
  • equivalent experience

Nice to have

  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience
  • Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience
  • equivalent experience
  • Extensive experience building and shipping large-scale machine learning systems in search, recommendation, ranking, advertising, commerce, conversational AI, or personalization.
  • Deep expertise in modern machine learning, including deep learning, transformers, representation learning, retrieval systems, recommendation systems, and foundation models.
  • Demonstrated experience serving as a technical lead for large-scale cross-organizational initiatives.
  • Proven ability to translate research innovations into production systems with measurable business impact.
  • Experience with LLMs, SLMs, multimodal AI, and agentic systems.
  • Experience in advertising, e-commerce, shopping, recommendation, or marketplace ecosystems.
  • Experience developing AI-powered assistants, commerce experiences, or personalization platforms.
  • Experience optimizing distributed training and inference systems on large GPU clusters.
  • Experience mentoring principal-level engineers, scientists, and technical leaders.

What the JD emphasized

  • drive the science vision, technical strategy, and execution
  • deep hands-on in machine learning innovation
  • proven record of technical leadership
  • large-scale production deployment
  • technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives
  • Define and execute the science roadmap
  • Lead end-to-end ML development
  • deliver scalable, reliable, and cost-efficient AI systems
  • Shape the technical vision for future agent experiences
  • Drive measurable improvements in customer satisfaction, engagement, relevance quality, and business outcomes
  • mentoring principal-level engineers, scientists, and technical leaders

Other signals

  • driving end-to-end innovation from research and experimentation through large-scale production deployment
  • technical leader across multiple science areas
  • deep hands-on in machine learning innovation
  • partner with engineering and product teams to deliver scalable, reliable, and cost-efficient AI systems