The unified context layer for enterprise data. Backed by Y Combinator.
About the role
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.
What they'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
More about this 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).
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.
We're a small, distributed team that works closely together. We pair on hard...
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