In 2026, we launched Computer, the defining product for the new era of agentic AI. Millions of people now use Perplexity to transform knowledge into action, and the Growth team owns the product loops that help them discover, adopt, and build lasting habits around our most important AI experiences.

As a growth engineer at Perplexity, you'll work across the full funnel — activation, onboarding, SEO, lifecycle, conversion, retention, and paid upgrade moments — while also building the applied AI systems that make those experiences smarter. Engineers are responsible for end-to-end projects from training and productionizing classifiers to identify high-intent users, personalizing value discovery, shipping contextual onboarding experiments, or wiring new AI capabilities into measurable adoption and revenue outcomes.

Growth at Perplexity goes beyond incremental funnel optimization. The team sits close to the core product - we translate behavioral signals, model outputs, and fast experimentation into durable product surfaces used across consumer and enterprise workflows. You'll move fast, run many experiments in parallel, and own the outcome end-to-end.

Tech Stack: TypeScript | Next.js | React | Python | PostgreSQL | Eppo | AWS

Why Perplexity is different

• Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious.

• Ownership. You identify the problem, design the solution and ship it.

• Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users.

• Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products.

• Partnership. We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence.

What you'll do

• Design, build, and own growth surfaces across the full funnel — activation, onboarding, SEO, lifecycle, conversion, retention, and paid upgrade moments — for Computer and every Perplexity product.

• Lead experiments end-to-end, from hypothesis and instrumentation through implementation, analysis, and rollout, shipping many in parallel and learning quickly from each.

• Build applied AI systems that power growth: classifiers for high-intent segments, personalization for value discovery, and AI-driven onboarding and recommendation