The role
From Perplexity's own posting.
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 surfaces.
Launch vertical-specific experiences (e.g., students, enterprise organizations) that connect new AI capabilities to measurable adoption and revenue outcomes.
Hill-climb on the metrics that matter: activation rate, paid conversion, retention, and habit formation, instrumented with clean data and trustworthy experiment readouts.
Partner closely with Product, Design, Data Science, Monetization, Marketing, and Applied AI to translate behavioral signals and model outputs into durable product surfaces.
Qualifications
2+ years of professional software engineering experience (we're open to strong junior and mid-level candidates with a track record of shipping).
Full-stack engineering skills, with comfort across a modern web stack (we use Next.js, React, TypeScript on the frontend and Python on the backend).
Strong execution: you ship many experiments and product improvements in parallel and drive them to a clear outcome.
Familiarity with A/B testing and experimentation platforms (Eppo, Statsig, Optimizely, or in-house equivalents).
Comfort with SQL and data-informed decision-making — you can pull, segment, and interpret your own funnel data.
Strong product judgment; you translate user behavior and growth opportunities into simple, effective technical solutions.
Self-motivated with strong ownership instincts; you propose experiments, ship features ahead of schedule, and drive improvements without asking for permission.
Genuine interest and adoption of AI products and willingness to learn quickly.
Nice to have
Direct experience on a growth, activation, conversion, retention, or lifecycle team at a consumer or PLG/self-serve SaaS company.
Experience training and productionizing ML models (classifiers, ranking, personalization) and connecting them to product surfaces.
Experience with SEO, lifecycle marketing tooling, or paid acquisition surfaces.
Familiarity with subscription and/or usage based billing products, paywalls, or paid upgrade experimentation.
Experience shipping for both consumer and enterprise audiences.
Time spent at a fast-growing startup or on a high-ownership engineering team.
Familiarity with mobile development - Swift/Objective-C/Kotlin/Java, in-app payments, mobile release cycles, Apple App Store and/or Google Play Store guidelines, and rollout monitoring