About the role
Zep manages, governs, and serves agent memory at enterprise scale. Enterprises build on Zep to run reliable, personalized agents across the business: millions of Context Graphs, served in under 200ms, inside their own VPCs and cloud deployments. Customers include Samsung, Zscaler, Twin Health, HoneyBook, and NASDAQ 100 and Fortune 500 technology companies. We also build Graphiti, our open-source context graph framework (30K+ GitHub stars).
You will own how developers and engineering leaders find Zep, understand it, and decide to build on it: the marketing site, the landing pages, the instrumentation underneath them, the automation that runs demand generation, and the agent tooling that lets one person do the work of a marketing team.
We're hiring an engineer who writes code most days and owns the marketing outcomes that code produces. There is no marketing team to inherit and none to manage.
We will measure you on signups and on the pipeline your systems produce.
You'll report to our founder, Daniel (2x founder, engineer, former head of ML at SparkPost), and join a team with pedigree at Scale AI, Dropbox, ActiveCampaign, DroneDeploy, and McKinsey.
How we work
We're a small, distributed team that works closely together. We pair on hard problems, review each other's designs, and treat learning as part of the job rather than something that happens after hours. We ask a lot of questions: of customers, of teammates, of our own assumptions. When we find pain, we go fix it.
We expect the same back: ask questions early, push back when you disagree, and care about the people on the other end of the API.
What you'll do
- Ship the marketing site, landing pages, and campaign surfaces as code. You build them and rebuild them when the data says to.
- Instrument every public surface from first touch through signup and into activation. Expect to rebuild some of this in your first month.
- Own answer engine discoverability. Developers ask Claude and ChatGPT before they ask Google, and their coding agents increasingly ask on their behalf. Structured data, llms.txt, docs architecture, and the measurement that tells us whether any of it works.
