Martin Duncan - Founding Product Lead, Enterprise

You’ll build our first enterprise products from the model up, turning frontier AI capabilities into solutions that enterprise 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 a mature product to optimize. There is no product yet. You’ll define which customers and workflows to pursue, spend 30–40% of your time embedded with customers and forward-deployed engineers in live work, and build agentic products, not traditional SaaS. It needs a senior, highly autonomous PM who’s genuinely AI-fluent and comfortable building from zero. If you need scaffolding and clear directives, this won’t fit.

What You’ll Own

• Define the first product opportunities across enterprise segments: customer profiles, workflows, jobs-to-be-done, and unmet needs in target industries.

• Translate our models and APIs into clear product definitions that serve real commercial customers.

• Help build the cross-functional operating model across research, engineering, go-to-market, and forward-deployed teams, then drive execution within it.

• Partner directly with design partners and early enterprise customers to validate opportunities and shape direction.

• Bring market signals back to research so model priorities reflect real commercial needs from day one.

• Build agentic products that generate revenue early and scale, with a GM’s focus on commercial viability.

First 90 Days

One way the first 90 could unfold:

• Days 1–30, Immerse & Diagnose: Learn the models, map the target verticals, and line up the first design partners.

• Days 30–60, Ship & Validate: Define and validate the first enterprise product opportunity in live customer work.

• Days 60–90, Scale & Systemize: Turn an early win into a repeatable product, and set up feedback loops to research.

What You Bring

• Staff or Principal-level product management experience building enterprise products in high-ambiguity environments.

• A track record of shipping enterprise AI applications, especially agentic products with real adoption.

• Functional AI/ML fluency: reasoning about capabilities, evals, latency and cost trade-offs, and fine-tuning versus prompting versus 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 a self-starter mentality (the top trait here), plus consultative skill with customers.

• Experience at both large companies and early-stage startups.

Nice to Have

• Experience on frontier model teams or at an early-stage AI lab.

• A founder or founding-team background.

• Experience at enterprise AI companies 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.