Principal Product Manager

Microsoft Microsoft · Big Tech · United States · Product Management

Principal Product Manager for Azure HPC & AI compute platform, focusing on defining and delivering hardware, software, and services for extreme scale AI training and inference, traditional HPC, and other technical computing workloads. Requires expertise in AI, complex distributed systems, and customer/market analysis using AI tools.

What you'd actually do

  1. Leverages AI to anticipate customers’ unmet or unknown needs and market opportunities for enhancements or development and new products/services of multiple feature areas (e.g., product, service) across divisional boundaries via quantitative (e.g., usage, telemetry) and qualitative analyses (e.g., customer usage, various listening systems, feedback channels of market and industry trends), analyzes security and ethical compliance, looks for patterns in the data across a portfolio, and scopes the impact and prioritization of a problem.
  2. Acts as a subject matter expert in performing market or user research through AI in collaboration with the User Research team, conducting competitive analyses, and examining market and industry trends, as well as industry-specific requirements or regulations.
  3. Uses AI for trend analysis and sentiment evaluation to generate hypotheses, understand problems, and predict customer preferences, leading experiments and A/B tests to improve model performance and inform feature development.
  4. Defines and optimizes AI-driven solution options across multiple feature areas across divisional boundaries (e.g., product, service), incorporating insights into refinement and experimentation processes.
  5. Collaborates with Product Marketing, Business Planning, and Engineering to identify product release criteria (including security requirements such as Security Development Lifecycle (SDL) compliance), customer acquisition, usage, retention, and monetization strategies.

Skills

Required

  • Product management
  • AI/ML knowledge
  • Market analysis
  • Customer research
  • Distributed systems
  • Technical computing
  • HPC
  • AI training
  • AI inference
  • Stakeholder management
  • Cross-functional leadership

Nice to have

  • Security Development Lifecycle (SDL) compliance
  • Quantitative analysis
  • Qualitative analysis
  • Competitive analysis
  • Contract negotiation
  • Partnership development
  • Acquisition target assessment

What the JD emphasized

  • expertise in AI
  • complex and distributed computer systems
  • security requirements
  • AI-driven solution options

Other signals

  • AI for product management
  • customer needs analysis
  • market opportunity identification
  • technical computing workloads
  • AI training and inference