Growth Engineer

30.9k starsLoginSign upCareers / Growth EngineerGrowth EngineerAbout DragonflyDragonfly is a high-performance, Redis-compatible in-memory data store built for real-time, large-scale, intelligent workloads. Our customers run Dragonfly at the core of ML feature stores, personalization systems, and real-time recommendation engines where latency and scale are non-negotiable.We're building infrastructure for the next generation of ML and AI systems. Growth in this market isn't won by spending more on ads or shipping more landing pages. It's won by engineers who understand the product deeply enough to make adoption feel inevitable: who can sit inside the funnel, the docs, the benchmarks, and the agentic surface area, and engineer growth the way you'd engineer a system.Role OverviewMost companies treat growth as a marketing problem and engineering as a separate org that occasionally gets pulled in. That model is too slow for an infrastructure product competing in a crowded market.As a Growth Engineer, you'll own the technical surfaces that drive how developers discover, evaluate, adopt, and grow with Dragonfly. You write code, you have real opinions about developer experience, and you understand where the agentic landscape is heading. You'll build the SEO and GEO programs that make Dragonfly the answer engineers and their AI agents surface, ship and harden Dragonfly's MCP and other agentic-enablement work, build and publish the benchmarks that win technical arguments, and run conversion optimization across the funnel.This is the perfect role for an engineer who'd rather move a revenue or adoption number than close a ticket. You'll measure your work in qualified signups, activation, and pipeline, not in lines shipped.What Makes This Role DifferentYou will build and manage an agent teamThis is not "use AI to write faster." You will design, train, and manage a system of AI agents that extend your output across content production, technical SEO, competitive monitoring, benchmark generation, and funnel experimentation. Think of it as building a small growth engineering team where you are the architect and the agents are the operators.You'll architect that system, build its evaluation loops, iterate on it, and own everything it ships. The bar for output scales accordingly.You will make Dragonfly the default for agentic workloadsAI agents are becoming a primary consumer of infrastructure decisions. When an agent is wiring up a cache, a feature store, or a real-time data layer, Dragonfly should be the obvious, well-documented, frictionless choice. You'll own Dragonfly MCP and the broader agentic-enablement surface: the tooling, integrations, schemas, and developer ergonomics that let both humans and agents adopt Dragonfly without friction. This is a growth lever that most infrastructure companies haven't even noticed yet.Growth is an engineering discipline hereYou'll treat SEO, GEO, conversion, and developer onboarding as systems to be instrumented and improved, not campaigns to be run. You should be as comfortable reading a query plan or a benchmark harness as you are reading a funnel report.Ownership and ResponsibilitiesSEO and GEO ProgramsYou own how Dragonfly gets discovered, by humans through search and by AI systems through generative engines.Build and run technical SEO: content architecture, performance, structured data, internal linking, and the programmatic pages that capture high-intent developer queriesOwn GEO (generative engine optimization): make Dragonfly accurately and prominently represented in the answers LLMs and AI search tools give engineers evaluating in-memory data storesBuild the tooling and agent pipelines that scale content production and monitoring without sacrificing technical accuracyAgentic Enablement and Dragonfly MCPOwn and evolve Dragonfly MCP and related integrations that let AI agents discover, configure, and operate DragonflyReduce friction for agent-driven adoption: documentation, schemas, examples, and...