Working at Zep

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 engineering leaders and developers find Zep, understand it, and decide to build on it: search and answer engines, the blog and website, email, and social. The founders set the content strategy and agents do the production work. You run publication and the campaigns around it.

We're hiring a marketer who runs enterprise B2B demand generation alone and uses agents to do the work of a team. There is no marketing team to inherit and none to manage.

We will measure you on signups and on the pipeline your campaigns 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

• Own search and answer engine discoverability. Buyers ask Claude and ChatGPT before they ask Google, and their coding agents ask on their behalf. Structured data, llms.txt, site and docs architecture, and the measurement that tells us whether any of it works.
• Run the blog and website publication pipeline. Content strategy and much of the long-form writing come from the founders. You run the calendar and adapt each draft to its channel before it publishes on schedule.
• Run email: lifecycle from signup to sales conversation, and outbound campaigns into enterprise accounts alongside sales.
• Run social in the places engineers and their leaders are, using agents to move fast and your own taste to decide what ships.
• Build and operate the agents that produce content, run campaigns, monitor competitors, and report results. You own them after they ship.
• Own the funnel measurement from first touch to signup, with our engineers on the instrumentation. Choose the stack and keep the cost of your own automation in view.

Your first 90 days

You start by publishing. By day 30 the blog and social cadence is running on an agent pipeline you built. By day 60 the funnel is measured from first touch to signup and you have a search and answer engine baseline to move. By day 90 you own the demand generation number and have a roadmap you can defend.

What we're looking for

1. You have run B2B demand generation for a technical product and can show the pipeline it produced.
2. You edit well and write when needed. Show us something you published that engineers read to the end.
3. You build with agents as a matter of routine. Claude Code, Cursor, or equivalent to produce content and run campaigns, as part of how you work rather than something you have tried.
4. You are comfortable in code: editing the site or wiring an integration. You do not need to have shipped applications.
5. You want to run a function alone, and you treat a team of one as an advantage.

Useful, not required: prior marketing at a dev tools, infrastructure, or AI company. SEO and AEO experience grounded in tests rather than theory. Paid acquisition. Familiarity with agent frameworks, retrieval, or graph databases.

This role is probably NOT a fit if:

• You run campaigns through agencies and contractors and have not done the production work yourself.
• You have not built with coding agents and do not intend to start.
• You want to hire a team before you have built the systems.
• You need an established playbook or sign-off before you move.

Compensation and benefits

Platinum medical, dental, and vision insurance. 401K with employer matching. Unlimited PTO. Flexible in-office culture in San Francisco, with remote options and periodic travel for team members outside the Bay Area.

How we hire

We respect your time and keep our interview process tight and focussed.

Screening Call (w/ Daniel, our Founder) → Team Calls (2-3 hours back-to-back, includes a portfolio walkthrough) → Decision Call (Daniel, again)

How to apply

Pick one campaign or content program you ran end to end. What pipeline did it produce, and what would you cut next time? (150 words or fewer.)

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).

What our customers' agents can reason about depends on the memory we retrieve and the memory we write. You own that loop. You build agents that improve retrieval. You finetune the models that extract memory, and the models that power those agents. You run the experiments and ship the result as production code.

We're hiring an engineer who builds agents and trains the models they run on.

We will measure you on whether retrieval and memory quality move, and on what reaches production.

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

• Build agents that improve retrieval: query understanding, ranking, and what to pull into context.
• Finetune the models that extract, update, and consolidate memory on Zep's domain.
• Finetune the models that power those agents, so the retrieval loop and the agent get better together.
• Own the work from dataset creation through experiment design, evaluation, training, and a change that ships to production. Our engineers work the serving path alongside you.
• Build the eval harnesses that catch regressions in retrieval, memory quality, and agent task completion before a release ships.
• Write up what you find, including the results that killed an idea, so the rest of engineering can build on it.

Your first 90 days

In your first week you pick up an open retrieval or memory-extraction problem and frame it as an experiment. By day 30 an agent or a finetune you built is in the product loop, or you have ruled the approach out and written up why. By day 90 you are three cycles in and the eval harness you built runs on every release.

What we're looking for

• You have shipped production ranking, retrieval, or query understanding.
• You've built classical ML into a real product — logistic regression, SVMs, GBTs, single-layer perceptrons.
• You've shipped finetuned models to production. You know transformer architectures and training workflows, and you work in PyTorch.
• You have shipped a non-trivial agentic system to production. Not a prototype, not a thin wrapper over a chat-completion API.
• You have a research methodology: dataset creation and curation, experiment design, and evaluation. You can frame an open problem and design an experiment that answers it.
• You have built evaluation for retrieval, generation, or agent tasks: gold sets, offline metrics, online tests.
• You write strong Python, and you have enough production AWS to ship, monitor, and iterate on what you train.
• You have a Master's in Computer Science or equivalent experience.

This role is probably NOT a fit if:

• Your shipped work is papers, prototypes, and demos.
• Your agent work is a wrapper over a chat-completion API, with no eval and no model you trained.
• You haven't finetuned a model that reached production.
• You need a research agenda handed to you before you move.

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 Zscaler, Samsung, Twin Health, HoneyBook, and NASDAQ 100 and Fortune 500 technology companies. We also build Graphiti, our open-source context graph framework (30K+ GitHub stars). Getting Zep into production in customer environments is its own engineering problem, and you own it.

You will lead our deployments and build the team that runs them. The first year is hands-on. You will be in architecture reviews and in our codebase, and on site when a deployment needs you. Our engineers work deployments alongside you, so you are never the only technical person on the call.

We're hiring an engineer and leader who has done this work and can now set the standard for how we do it.

This is note a core product engineering role. You should have significant customer-facing experience in an FDE, sales engineering, or field engineering role.

We will measure you on time from signature to production, and on custom engineering per deployment.

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

• Own the technical outcome for our largest accounts, from the first architecture conversation through 90 days in production.
• Write code. Integration work, reference architectures, deployment tooling, and fixes in the product itself.
• Deploy Zep inside customer infrastructure across Managed, BYOK, and BYOC, and work directly with the platform and security teams who have to approve it.
• Define the engagement model. Decide which accounts get embedded support, and set how that work is scoped and priced.
• Hire the team. You define the bar and lead the engineers you bring in.
• Reduce the custom work each deployment takes. What you build twice becomes a reference architecture or a product requirement.
• Take what you learn in production back into the roadmap. You will be in planning with the engineers who can act on what you bring.

Your first 90 days

You start inside a live deployment: shipping code, meeting the customers we already serve, and learning how Zep runs in their environments. By day 60 you own every active deployment. By day 90 you have written the first version of the engagement model.

\What we're looking for

• 10+ years across software engineering, forward deployed engineering, solutions architecture, or sales engineering, with several of those years as a hands-on engineer.
• You have taken enterprise deployments from first call to production under a security review and a fixed deadline.
• 3+ years leading customer-facing technical people, or a clear case for why you are ready to.
• Strong Python, plus Go or TypeScript. You will read our codebase and send patches.
• You have shipped a non-trivial agentic system to production. Not a prototype, not a thin wrapper over a chat-completion API.
• Comfortable in customer infrastructure: AWS, Kubernetes, Terraform, and the identity and encryption work a BYOC install requires.
• You raise risks early, including when it is inconvenient for the person you are talking to.

This role is probably NOT a fit if:

• You have run a professional services P&L and think of this as a delivery org.
• Your customer-facing work ends with the technical win and a handoff at signature.
• You haven't written production code in several years and don't intend to start again.
• You need an established playbook or sign-off before you move.