Product Manager Lead, Technical Intelligence, Deepmind

Google Google · Big Tech · Mountain View, CA +2

Lead a team of analysts to provide insights on AI infrastructure, economics, and frontier innovation, guiding Alphabet's capital allocation, planning, and technical roadmaps. This role involves analyzing AI labs, hyperscalers, and supply chains to estimate compute capabilities and financial sustainability, delivering data-driven diligence for strategic decisions.

What you'd actually do

  1. Direct, mentor, and scale a team of analysts; establish standards for cost modeling, tracking, and executive presentations.
  2. Analyze the roadmaps, partnerships, and model architectures of major AI labs, hyperscalers, and NeoClouds.
  3. Track external data center expansions, compute cluster acquisitions, and supply chain bottlenecks to estimate industry-wide training and inference capabilities and build response playbooks.
  4. Oversee detailed financial models analyzing performer pricing structures, burn rates, unit economics, and compute financing sustainability.
  5. Deliver first-principles narratives and briefings directly to Vice Presidents/Senior Vice Presidents (VPs/SVPs). Partner cross-functionally with Google Cloud, Infrastructure, Research, and Finance to provide data-driven diligence for Mergers and Acquisitions (M&A), compute partnerships, and infrastructure investments.

Skills

Required

  • Bachelor's degree in a quantitative or technical field (e.g., Engineering, Computer Science, Finance, Economics, Physics) or equivalent practical experience.
  • 10 years of experience with market intelligence, technical product management, equity research, or corporate strategy within the technology, semiconductor, or cloud infrastructure sectors.
  • Experience managing analysts or researchers.

Nice to have

  • CFA charterholder, or advanced degree (MBA, MS, or PhD) in a technical or financial field.
  • Direct experience in AI infrastructure strategy, cloud economics, or semiconductor-focused investment banking at a major AI lab or cloud provider.
  • Deep knowledge of the AI hardware ecosystem (TPUs, GPUs, ASICs, networking, data centers) or the Generative AI landscape.
  • Strong understanding of the semiconductor supply chain—including foundries and packaging—and its impact on cloud capacity.
  • Proven ability to present high-stakes analysis to C-suite and VP-level stakeholders.

What the JD emphasized

  • AI infrastructure strategy
  • cloud economics
  • semiconductor-focused investment banking
  • AI hardware ecosystem
  • Generative AI landscape
  • semiconductor supply chain