Careers at Zentrik
8 senior roles
Agent-native brains
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The Intelligence Brain

Build the research systems that turn customer conversations, market movement, and product evidence into better decisions.

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Product intelligence
Innovative role

The Intelligence Brain

Customer signal, market research, competitive systems, and synthesis

A product researcher, market analyst, and AI-powered intelligence operator shape.

Build the research systems that turn customer conversations, market movement, and product evidence into better decisions.

Start a conversation

A useful first note shows how you think and what you have made.

What you would own

  • A repeatable intelligence practice across calls, support, community signal, competitors, pricing movement, and buyer language.
  • Research agents, source-quality checks, synthesis rubrics, and artifacts that help the team decide what is worth doing.
  • Clear narratives that connect product bets to customer pain, market timing, and business impact.

What we would look for

  • You can synthesize messy qualitative evidence without flattening nuance.
  • You can use AI to increase research throughput while preserving judgment, source quality, and traceability.
  • You understand product management, SaaS markets, and how buyer language becomes positioning and roadmap signal.

Questions you would help answer

  • What market evidence should change Zentrik strategy this month?
  • How do we separate loud AI excitement from durable customer value?
  • What research artifacts help product, growth, and customer teams make sharper decisions?
What the work feels like

The product is the operating layer for better product decisions.

The role is not to add AI decoration to old workflows. The role is to preserve product intent from customer evidence to shipped work, while making every loop more inspectable, more reliable, and easier for a human team to trust.

Product intent stays human

AI can accelerate the work, but the reason to build still comes from evidence, taste, tradeoffs, and accountability.

Visible systems beat claims

We look for artifacts: GitHub repos, workbenches, eval notes, customer loops, market systems, prototypes, and operating docs.

The harness matters

Great AI-native work depends on context, evals, guardrails, review loops, and proof that the next cycle gets sharper.

Zentrik agent workflow graph showing product context moving into agent work.
How we think about fit

Range matters, but judgment matters more.

We are more interested in visible systems of work than in claims of AI fluency. Strong candidates can show how they create leverage without confusing output volume for customer value.

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

Show us the system behind the work.

A strong note could include a GitHub repo, product teardown, prototype, GTM system, research synthesis, eval harness, personal workbench, shipped product, or a short memo about where you think Zentrik should go.

Contact Zentrik
Tell us which role fits your strongest proof.
  1. 1

    Send a proof artifact

    Share a repo, product teardown, prototype, GTM system, research synthesis, eval harness, customer system, or short memo.

  2. 2

    Work through the role shape

    We will talk about the domain, 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 the initial domain clearly enough that you can start creating leverage quickly.