AdTechTalent
Data Science2 days agoOn-site

Microsoft

Principal Applied Scientist

machine learningAILLMsSLMsmultimodal AIrecommendationrankingpersonalizationconversational AIadvertisinge-commerceshoppingagentic systemsdistributed trainingGPU clusterstechnical leadership

Key details

Salary

Not specified

Employment type

Full-time

Seniority

Senior

Years experience

10+

Location

Mountain View, United States; Redmond, United States

Full job description

Microsoft Monetization is seeking a Principal Applied Scientist and Technical Lead to drive AI-powered advertising and commerce experiences. The role involves leading machine learning innovation in relevance, intent understanding, personalization, recommendation, and agent-driven commerce. Responsibilities include technical leadership, defining science roadmaps, end-to-end ML development, partnering with engineering and product teams, and mentoring staff. Required qualifications include advanced degrees in relevant fields with 5+ to 8+ years of experience, extensive experience in large-scale ML systems, expertise in modern ML techniques including LLMs and multimodal AI, and leadership experience. The position is full-time, on-site in Redmond, WA or Mountain View, CA. Salary ranges from $165,600 to $296,400 annually, with higher ranges for specific metro areas.

What you'll do

  • Serve as the technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives across Copilot, Shopping, and Ads experiences
  • Drive innovation in machine learning technologies including LLMs, SLMs, multimodal AI, retrieval, ranking, personalization, and recommendation systems
  • Define and execute the science roadmap for user intent understanding, product understanding, content relevance, and advertiser matching
  • Lead end-to-end ML development, including model architecture, training data strategy, evaluation, experimentation, calibration, and production deployment
  • Partner with engineering and product teams to deliver scalable, reliable, and cost-efficient AI systems
  • Shape the technical vision for future agent experiences, conversational shopping, and AI-assisted commerce scenarios
  • Drive measurable improvements in customer satisfaction, engagement, relevance quality, and business outcomes
  • Mentor scientists and engineers while raising the technical bar across machine learning, experimentation, and scientific rigor

Requirements

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience
  • OR equivalent experience
  • Preferred: Master's Degree AND 12+ years related experience
  • Preferred: Doctorate AND 8+ years related 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
  • 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

Tech stack

machine learningAILLMsSLMsmultimodal AIretrieval systemsranking systemspersonalizationrecommendation systemsdeep learningtransformersrepresentation learningfoundation modelsdistributed trainingGPU clusters

Benefits

Certain roles may be eligible for benefits and other compensationLink to additional benefits and pay information: https://careers.microsoft.com/us/en/us-corporate-pay

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