You'll build Luma's enterprise products from the model up, turning frontier multimodal capabilities into solutions marketing, advertising, and entertainment customers will pay for and scale. You'll operate like a general manager, making P&L-driven calls rather than executing handed-down directives.
This isn't inheriting a mature product to optimize. You'll define which customers and workflows to pursue, spend 30-40% of your time embedded with forward-deployed teams in live customer work, and build agentic products, not traditional SaaS. It needs a senior, highly autonomous PM who's genuinely AI-fluent. If you need scaffolding and clear directives, this won't fit.
What You'll Own- Define product opportunities across enterprise segments - customer profiles, workflows, jobs-to-be-done, and unmet needs in marketing, advertising, and entertainment.
- Translate Luma's models, Canvas, Agents, and APIs into clear product definitions that serve real commercial customers.
- Drive cross-functional execution across research, product, engineering, go-to-market, and forward-deployed teams in a pod structure.
- Partner directly with enterprise customers in meetings to validate opportunities and shape direction.
- Bridge market signals back to research so model prioritization reflects real commercial needs.
- Build agentic products that generate revenue now and scale, operating with a GM mindset on commercial viability.
First 90 DaysOne way the first 90 could unfold.
- Days 1-30 - Immerse & Diagnose: Learn the models, the enterprise verticals, and the highest-value workflows.
- Days 30-60 - Ship & Validate: Define and validate an enterprise product opportunity in live customer work.
- Days 60-90 - Scale & Systemize: Turn a bespoke win into a product that scales to more customers, with feedback loops to research.
What You Bring- Staff or Principal-level PM experience building enterprise products in high-ambiguity environments.
- Built successful enterprise AI applications, especially agentic products with real adoption, not just SaaS.
- Functional AI/ML fluency: reasoning about capabilities, evals, latency/cost trade-offs, and fine-tuning vs prompting vs tool use.
- Effectiveness across product, engineering, research, go-to-market, and customer-facing teams.
- A general-manager mindset with P&L experience, thinking in business trade-offs and revenue.
- Very high agency and self-starter mentality (the top trait here), plus consultative PM skill with customers.
- A mix of large-company and earlier-stage experience.
Nice to Have- Experience on multimodal model teams and products.
- Domain exposure in marketing technology, brands, advertising, or entertainment.
- Founder-type background.
- Experience at enterprise AI companies (Harvey, Sierra, Decagon, Glean, Clay) where customer-embedded PM is the operating model.
- A clear sense of the difference between a demo that wins a pitch and a product that runs in production.
