Careers at Zentrik / 8 open roles

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Data & Evals Systems Lead

Build the data quality, lineage, eval, and feedback systems that make AI-assisted work trustworthy.

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8
open roles
4
role paths
1
small team

Data systems

Zentrik evidence graph with source-backed product context.

Work context

Roles connect to real product systems

Hiring brief

Data & Evals Systems Lead

Data quality, knowledge systems, model behavior, and evaluation practice

You bring
Strong judgment and visible work
You own
A clear role with real company scope

Data systems / Builder role

Data & Evals Systems Lead

Data quality, knowledge systems, model behavior, and evaluation practice

A data and evaluation role for someone who can make AI-assisted work measurable and trustworthy.

Build the data quality, lineage, eval, and feedback systems that make AI-assisted work trustworthy.

Start a conversation

A concise note is enough to start.

What you would own

  • Data quality, source lineage, context modeling, evaluation sets, and feedback loops for product intelligence workflows.
  • Systems that measure model behavior, recommendation quality, retrieval quality, cost, drift, and customer-facing reliability.
  • Tools and review practices that make non-deterministic work more inspectable, comparable, and trustworthy.

What we would look for

  • You can reason across data modeling, product semantics, eval design, analytics, and AI workflow reliability.
  • You understand that evals are product infrastructure, not an afterthought.
  • You can build practical harnesses that help humans decide whether the system is getting better.

Questions you would help answer

  • How should Zentrik know that a recommendation is actually improving?
  • What evidence and lineage should follow an insight from signal to shipped work?
  • How should evals, analytics, and human review reinforce each other?
Zentrik workflow graph showing product context moving into agent work.

What the work feels like

Work here should feel direct, concrete, and high-trust.

We are a small team building a product for product teams. You should expect to work close to customers, code, evidence, product decisions, and the systems that help the company learn faster.

High ownership, small team

You should expect real scope, close collaboration, and enough context to make decisions without waiting for layers of approval.

Artifacts matter

We learn more from repos, prototypes, teardown notes, customer loops, growth systems, eval harnesses, and operating docs than from generic claims.

AI is part of the work

We use AI seriously, but the bar is better judgment, clearer systems, faster learning, and work that can be reviewed.

How we think about fit

Show us how you think and what you have made.

We do not need a polished application package to begin. Strong candidates can point to visible work and explain the choices, tradeoffs, and lessons behind it.

  • GitHub or public work artifacts

  • Agent harness or eval loop

  • Product or business judgment

  • Market or customer learning system

  • Infrastructure or MLOps system

  • Community or enablement motion

  • Personal workbench or knowledge system

Hiring process

Start with a short note and one useful artifact.

Tell us which role you are interested in and why. Include a repo, product teardown, prototype, GTM system, research synthesis, eval harness, shipped product, or short memo if it helps us understand your work.

Contact Zentrik
Tell us which role you want to explore.
  1. 1

    Send a focused note

    Tell us which role fits and include one or two artifacts: a repo, product surface, teardown, GTM system, research synthesis, eval harness, or short memo.

  2. 2

    Talk through your work

    We will talk about the role, the level of ownership, the systems you have built, and where your judgment is strongest.

  3. 3

    Review a real operating problem

    The useful signal is how you structure ambiguity, design feedback loops, and decide what is worth doing next.

  4. 4

    Define the first scope

    If there is a fit, we will shape an initial domain with clear ownership, expectations, and success criteria.