Product Manager, Growth

LumaRedwood City, CA

About The Position

You'll own growth end to end at Luma — acquisition, activation, retention, and revenue — scaling the product from strong early traction toward mass adoption, and defining how growth works here from first principles. Growth at Luma runs across product, engineering, marketing, and data, and you'll operate with high velocity, imperfect data, and a bias to action. It fits an experiment-driven PM who's equally strategic and hands-on. If you need clean data and an established growth function to step into, this isn't that yet.

Requirements

  • 5–10+ years of product management with a strong growth focus.
  • A track record driving measurable growth (activation, retention, revenue).
  • Deep understanding of product-led growth, funnels, loops, and lifecycle optimization.
  • Strong intuition for high-leverage opportunities in ambiguous environments.
  • An experiment-driven mindset with A/B testing and rapid iteration.
  • Comfort operating strategically and tactically, with incomplete data.
  • Experience partnering with marketing on paid growth and attribution, familiarity with pricing and packaging, and the technical fluency to unblock execution.

Nice To Haves

  • Experience building or growing products for creative professionals.
  • Familiarity with Figma, Canva, Adobe, or 3D/creative platforms.
  • Experience on AI-native products or in fast-moving startups.

Responsibilities

  • Own and execute product-led growth across the full funnel, from acquisition to revenue.
  • Identify and prioritize high-leverage (80/20) opportunities that drive outsized growth.
  • Design and scale rapid experimentation loops across onboarding, activation, retention, and monetization.
  • Build repeatable, measurable growth loops, and improve first-time experience so users reach value fast.
  • Partner with marketing on paid acquisition, attribution, conversion, pricing, and packaging.
  • Work hands-on with engineering to prototype, ship, and iterate, making fast decisions with limited data.
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