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Zuddl is the unified event platform that B2B marketing teams at companies like CrowdStrike, Figma, Stripe, TikTok, Check Point Software Technologies, and Iterable use to run their most important conferences, field events, and webinars — all from one system.
We’re backed by Y Combinator, Alpha Wave Global, and Qualcomm Ventures, and we’re going after every legacy player in a market that’s being rebuilt from scratch.


Where We Are

Zuddl has already done the hard, unsexy work.
Product data, CRM, engagement signals — it’s all unified and structured. We know what’s happening across the customer lifecycle.
What we don’t have yet is a system that acts on it.
Right now, humans still sit in the middle:
  • Reading signals
  • Deciding what to do
  • Coordinating execution
That’s the gap. That’s what this role closes. You’ll report directly to our Head of Revenue & Business Operations and work with near-founder autonomy from day one.


What Makes This Different From Every Other “AI + RevOps” Role


Most of those roles are sales ops with a ChatGPT wrapper.
You’d be:
  • Configuring tools
  • Building Zapier flows
  • Handing reports to someone else to act on
This isn’t that.
The difference is in what the output actually does.


What We’re Not Asking You to Build

  • Dashboards
  • Prompt layers
  • Workflow triggers that a human still has to review
  • Anything where the system produces an insight and stops there

What You’ll Actually Build

Systems that:
  • Ingest signals
  • Reason about them
  • Execute across CRM, email, and product systems
…without needing a human to coordinate.
The GTM team should rely on what you build the same way they rely on infrastructure.


What You’ll Own

Three layers. All yours.

1. Context

  • Define what signals matter.
  • Structure data across the GTM and product lifecycle so it’s actually usable for decisions — not just available.

2. Decisions

  • Build agents that decide what should happen next.
  • Not triggers — reasoning.
  • Example: “This account just hit three expansion signals and went quiet on support. Here’s what fires.”
Feedback loops should be built in from the start.

3. Execution

  • Automate the actions.
  • CRM updates, outreach, internal alerts — whatever the system decides should happen, happens.
  • No human coordination required.

In Practice

You’ll build things like:
  • Prospecting systems that run without SDR babysitting
  • Deal systems that surface next actions automatically
  • Expansion signal systems that don’t depend on someone remembering to check

What We’re Looking For

  • Strong Python or JavaScript skills — you write real code, not glue scripts
  • You’ve built AI workflows or agent systems that actually shipped and ran
  • Comfortable with APIs, data pipelines, and designing systems from scratch
  • 1–2 years of engineering experience
  • Curious about how GTM and revenue actually work
  • You can take a messy, underspecified problem and turn it into a working system — without someone handing you requirements
  • You ship fast, find out it’s wrong, and fix it in the same week

You’ll Be Filtered Out Fast If

  • Your AI experience is prompt engineering or no-code tools only
  • You’ve never shipped something another person or process depends on
  • You need a spec before you can start
  • Your default is to wait for clarity rather than go find it
  • You think adding AI to an existing workflow counts as building a system


Where This Goes

We’re not going to script a career path for you.
It depends on:
  • What you want
  • What you build
What we can say:
  • You’ll get the kind of ownership and strategic exposure most engineers don’t touch until much later
  • You’ll understand how revenue actually works
  • You’ll see where systems break under pressure
  • You’ll experience what it feels like to build something a company depends on
If you want to start something eventually, this is genuinely good preparation.
If you want to go deep on GTM systems as a craft, there’s a lot of runway.
If you want to grow into a leadership role as we scale, that’s a real path too.


What’s Not on Offer

  • A defined ladder
  • A long ramp-up
  • Work that doesn’t matter yet

What Good Looks Like at 90 Days

  • At least one manual GTM workflow has been replaced by a system
  • Pipeline generation is partially automated — signal to outreach without SDR input
  • Expansion signals surface on their own, not through someone’s memory
  • The GTM team is asking:
“Can the system do this?”
instead of doing it themselves

Why You Want To Work Here


  • Competitive compensation
  • Employee Friendly ESOPs
  • Remote Working
  • Flexible Leave Program
  • Home Workstation Setup
  • A culture built on trust, transparency, and integrity
  • Ground floor opportunity at a fast-growing series A startup

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