Principal Product Manager

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Product Management

This Principal Product Manager role within Microsoft AI focuses on defining the vision, strategy, and execution for AI training pipelines. The role involves leading cross-functional teams to build scalable and high-quality training data collection and processing systems, defining success metrics, and ensuring compliance. It requires deep collaboration with engineering, research, and data science to operationalize new evaluation techniques and metrics for LLM and ML-based products, with a strong emphasis on data processing, infrastructure, and evaluation systems.

What you'd actually do

  1. Define the multi‑year product vision and strategy for MAI’s Training Pipelines, related Success metrics, Feedback loops, and Testing mechanisms.
  2. Create strategies for collecting training data for next generation documents, understanding models that are multi-modal and non-HTML like PDF.
  3. Define success criteria, OKRs, and KPIs across multiple product surfaces and organizational boundaries.
  4. Establish methodologies for Deep Learning and AI infrastructure and platform support than engineers can use.
  5. Research, design, and operationalize new evaluation techniques and metrics that improve the quality and reliability of LLM and ML‑based products.

Skills

Required

  • Bachelor's Degree AND 8+ years experience in product/service/program management or software development OR equivalent experience.

Nice to have

  • Bachelor's Degree AND 12+ years experience in product/service/program management or software development OR equivalent experience.
  • 4+ years experience taking a product, feature, or experience to market (e.g., design, addressing product market fit, and launch, internal tool/framework).
  • 6+ years experience improving product metrics for a product, feature, or experience in a market (e.g., growing customer base, expanding customer usage, avoiding customer churn).
  • 6+ years experience disrupting a market for a product, feature, or experience (e.g., competitive disruption, taking the place of an established competing product).
  • Outstanding Analytical skills.
  • Experience in writing good detailed technical specifications for all new features.
  • Good Understanding of LLM, Big Data, Machine Learning, and Data Mining.
  • Understanding of Security, Privacy and Compliance practices.
  • Attention to detail, planning, organization, and project management skills .
  • Ability to quickly adapt to new technology and go deep in new focus areas.
  • Excellent oral and written communication skills with the ability to collaborate effectively with a large number of partners and customers and translate requirements into a product vision and roadmap.

What the JD emphasized

  • customer-first organization
  • operate several steps ahead
  • trillions of documents
  • pushes the boundaries of our infrastructure and computational resources
  • interesting challenges that haven't been encountered before
  • next generation of LLM, NLP, deep learning
  • large scale distributed systems
  • informational retrieval techniques
  • scalable and high-quality training pipelines
  • defining KPIs for success
  • deep data analysis
  • writing product specifications
  • delivering complex large projects
  • operational complexity
  • intersection of people, process, and product quality
  • build compliant, scalable, high-quality AI systems
  • multi-year product vision and strategy
  • collecting training data
  • multi-modal and non-HTML like PDF
  • Deep Learning and AI infrastructure and platform support
  • platform architecture decisions
  • capacity planning and optimization
  • ensure platforms are compliant-by-design
  • improve labeling workflows
  • model-as-a-judge systems
  • agentic evaluation pipelines
  • operationalize new evaluation techniques and metrics
  • improve the quality and reliability of LLM and ML-based products
  • Go deep into new technical areas quickly

Other signals

  • building AI-powered products
  • next generation of LLM, NLP, deep learning
  • large scale distributed systems
  • informational retrieval techniques
  • scalable and high-quality training pipelines
  • defining KPIs for success
  • deep data analysis
  • writing product specifications
  • delivering complex large projects
  • operational complexity
  • intersection of people, process, and product quality
  • build compliant, scalable, high-quality AI systems
  • multi-year product vision and strategy
  • collecting training data
  • multi-modal and non-HTML like PDF
  • Deep Learning and AI infrastructure and platform support
  • platform architecture decisions
  • capacity planning and optimization
  • ensure platforms are compliant-by-design
  • improve labeling workflows
  • model-as-a-judge systems
  • agentic evaluation pipelines
  • operationalize new evaluation techniques and metrics
  • improve the quality and reliability of LLM and ML-based products
  • Go deep into new technical areas quickly