Perplexity
Growth Manager, Enterprise Lifecycle
● Verified active
- Employment type
- Full-time
- Location
- San Francisco
- Published
- Jul 16, 2026
- Last checked
- Oct 26, 2026
About this role
Perplexity is seeking an Enterprise Product Growth Manager to own the full nurture and engagement engine for our enterprise and business buyers — from the first outbound touch through closed-won and into expansion. You will partner closely with Demand Generation, Sales, Product Marketing, and Revenue Operations to move accounts through every stage of the funnel, and you will build the AI-native tooling that makes those programs run.
We are not looking for someone to operate yesterday's marketing stack. If you are the kind of marketer who writes their own SQL, prototypes their own agents, and treats every flow as something to automate rather than execute by hand — this role was written for you.
RESPONSIBILITIES
- Develop and own Perplexity's end-to-end enterprise lifecycle user states — outbound nurture, MQL-to-SQL conversion, opportunity acceleration, post-sale activation, expansion, and renewal — with direct accountability for pipeline created, pipeline progressed, and revenue retained.
- Build and operate AI-native lifecycle tooling — agents that segment audiences from the warehouse, personalize copy at the account and persona level, choose channel and send time, QA before send, and read out results — replacing manual work in our stack wherever it appears.
- Design and run multi-channel nurture across email, in-product, notifications, Slack, and emerging surfaces, tailored to enterprise personas with content tied to where each account sits in the funnel.
- Partner with Demand Generation and Sales on top-of-funnel outbound: turn cold and intent-signaled accounts into engaged conversations, and instrument the handoffs that move them to opportunity.
- Own post-sale lifecycle: admin onboarding, end-user activation, feature adoption, at-risk intervention, and expansion plays that grow seats and ARR inside existing accounts.
- Run every program as an experiment. Define the hypothesis, the control, the primary metric, and the readout. Kill what doesn't work, double down on what does.