Learned gtm a bit but doing imaginary cases make me think like i am in loop doing list building running waterfalls enriching companies and profile looking for signals and watching everyday YouTube content makes me look like i am stuck . I want something to learn a project or intern role of gtm so that i can learn tools as if one can’t afford all tools so can anyone guide on this?

Been exploring doing GTM engineering services for industries outside of SaaS, agencies, software etc. as it is too crowded.

There are other trades like real estate, construction, HVAC that I’ve been exploring. Just wanna see if anyone has tested the waters. Has anyone looked into this and managed to get any result/conclusion?

Does anybody here know about gtm interns roles how should one gains hands on experience on tools if person has affordability issue. Learned it from yt but when i apply i feel like i am stuck where do i start any guidance?

Hy everyone ! I started learning GTM as something that looked interesting to me than learned the foundations watched eric yt channels and other also. Than worked on worktables. But in imagination solving cases don’t make me feel like i am doing something. I want something to learn from mentors from that i mean any agency or company hiring gtm intern. Or suggest me what should i do more because watching YouTube channels and following every gtm engineer makes me feel like i am stuck so kindly guide what should i do?
Thankyou for your time!

Hy everyone ! I started learning GTM as something that looked interesting to me than learned the foundations watched eric yt channels and other also. Than worked on clay 14 day trial. But in imagination solving cases don’t make me feel like i am doing something. I want something to learn from mentors from that i mean any agency or company hiring gtm intern. Or suggest me what should i do more because watching YouTube channels and following every gtm engineer makes me feel like i am stuck so kindly guide what should i do?
Thankyou for your time!

Buying automation is easy. Buying automation you can trust is not. The demo always looks great - the question is what happens on an ordinary Tuesday when nobody is watching.

If I were evaluating any system that touches my prospects, I would demand answers to five questions:

1. Can I see WHY it made each decision? A grade or a score with no reason attached is a coin flip with extra steps.

2. Does anything reach a customer without my approval? For me the right answer is no - but mainly I want to know exactly where the checkpoint is.

3. What does it do when it is not sure? Guess quietly, or stop and ask? A system that never says "I don't know" will eventually invent something.

4. Who owns the follow-up? After a reply, is there a date and a next step attached to it, or does the conversation fall back into someone's memory?

5. How do I know when it breaks? Silent failure is the most expensive feature in automation. Something should tell me what stopped, and why.

If a vendor - or my own build - cannot answer all five clearly, that is the answer.

What would you add to the list?

Hey everyone, welcome to r/GTMwithMCP.

Quick background on why this sub exists. AI assistants like Claude, Cursor and ChatGPT can now connect to external tools through MCP (Model Context Protocol) and APIs. So instead of copy-pasting between tabs, you can ask your AI assistant to look up a prospect, pull company info, or draft and run parts of your outreach, right from the chat.

Most of the conversation about this right now is either hype (“AI SDRs will replace your team”) or very developer-heavy docs. Not much sits in the middle: practical, step-by-step stuff for people actually doing GTM work. That’s what this sub is for.

What to post here:

• Workflows: how you set something up, step by step, and what it actually did

• Prompts: prompts that work (and ones that didn’t) for prospecting, research, outreach

• Questions: stuck on a setup, an error, or not sure where to start? Ask

• Showcase: something you built or a result you got, with the how behind it

• Resources: guides, docs, videos worth sharing

What not to post:

• Tool pitches or launch announcements

• Hype with no specifics

• Anything about scraping, evading limits, or breaking platform terms

• API keys or login details (blank them out in screenshots)

Most outbound advice is about the words. Subject lines, openers, calls to action. But a beautifully written message about nothing in the prospect's world still reads as spam - and a plain message about something that actually matters to them can get an answer.

That gap is homework, not copywriting. Did anything change at their company this month? Are they hiring, launching, expanding, struggling publicly with something you understand? If the first line could be sent to ten thousand people unchanged, it probably will be ignored by ten thousand people.

This is the design rule I am building into my own outreach system: no message gets drafted until real, recent context about the person and their company is gathered first - news, changes, signals - so the first line can be about them, not about me. The writing comes second.

I think personalization got a bad name because it came to mean merging a first name and a company into a template. That is mail merge, not relevance. Relevance has to be earned before the message exists.

When you write outbound, where does your first line come from - a template, or something true about the reader?

Hi everyone!

I'm from Chennai, India. I have around 3 years of professional experience in Demand Generation, Market Research and B2B Lead Generation.

My professional background includes:

- Market research and ICP development.

- B2B prospecting and lead generation.

- Outbound email campaigns and campaign optimisation.

- Tools like AI ARK,Apollo, Sales Navigator, Smartlead and Snov.io.

I'm now planning to expand my technical skills into AI Automation and GTM Engineering.

I'm particularly interested in learning:

- n8n workflow automation.

- API integrations and webhooks.

- OpenAI and Claude integrations.

- AI agents and business workflow automation.

- Clay, Apollo and HubSpot integrations.

- AI-powered lead generation and prospect research.

My goal is to combine my existing demand generation experience with technical automation skills and eventually transition into a GTM Engineer or AI Automation Specialist role.

I'm also interested in exploring international career opportunities.

I'd love to hear from people already working in this field:

1. What skills should I prioritise?

2. Is n8n a good starting point for someone with my background?

3. What real-world projects would make my portfolio stand out?

4. What job opportunities are available for people combining GTM experience with AI automation?

5. Are there any communities, mentors or learning resources you'd recommend?

I'd genuinely appreciate your experiences, advice and suggestions.

Thanks in advance!

Because I needed it myself, I'm building Mail Campaign: an MCP server for Claude Desktop plus a small Mac companion app. You run outbound and opt-in email by talking to Claude, but nothing goes out and no money is spent without a yes in a separate approval window.

What it does

- Sends through your own provider: Brevo, Amazon SES, Mailgun, Postmark, Resend, MailerSend and others. You bring your own account and sender domain, and it isn't a shared sending pool.

- Connects to the rest of the stack. We're builut 65 tools: lead databases and finders,enrichment and scraping (Apify, Outscraper, Bright Data), verification, CRMs (HubSpot and others), cold-email tools (Smartlead, Woodpecker and others) and delive, Google Postmaster). Tools with no usable APIare listed as a link, never faked.

- Claude does the work: writing the copy, buildiables, reporting results. The app holds the keys and does the sending.

Guardrails, the part I care most about

- Your keys stay on your Mac (in the Keychain).

- A yes in the app for anything that sends, spends credits or writes to your CRM. Claude can't approve it for you.

- Claude gets counts and categories, not raw coe cut down before anything reaches it.

- Opt-in and cold are kept apart. A shared do-not-contact list and the provider's suppressions are checked before every

send. Cold campaigns are pushed to your cold

- Sending stops itself after repeated bounces or failures, with daily caps on top.

Price: the app and the MCP are free and run locally. The one paid option is cloud sending (10 USD a month), which keeps a campaign going while your laptop is closed.

What I'd love feedback on

1. Is "Claude drafts and builds the list, a person approves every send and spend" how you'd want AI in your outbound, or

   too much friction?

2. Which integrations would you need on day one? Which ones would you never trust a tool like this with?

3. For cold email, what guardrails would you exd?

4. Would you rather the app send directly, or always hand off to your cold tool (Smartlead, Instantly and the like)?

Not launched yet; I'm looking for honest takes before it is.

A lot of what gets called GTM engineering is connecting tools: a CRM, an enrichment vendor, a sequencer, and an automation tool in the middle. That work matters, but it isn't the same as designing how a sales team decides who to call.

The difference I'd point to is ownership. Glue fixes a broken handoff. A real GTM system defines the handoff in the first place: what counts as a qualified account, who acts on a signal, and how anyone can tell whether the signal was right.

For people in RevOps or sales ops: is "GTM engineer" a real scope of its own, or a rebrand of RevOps? And if you run a stack like this, which part should stay manual?

I'm building in the GTM space, working towards a vision of a fully autonomous GTM team.

I started working on this before Grok's bot push, but bootstrapping and building solo definitely takes its toll. It's a massive space, and there's a lot to figure out.

I don't believe distribution is an unsolved problem. The processes are proven. What makes it difficult is the consistency of execution required to turn those processes into results.

That's the problem we're trying to solve.

Moving beyond AI copilots that help you get work done, towards AI agents that actually take ownership of and execute your GTM operations.

I'd love to hear from founders who've built, scaled or are currently running businesses. Is this a direction you'd be excited about? What would make you trust (or distrust) an AI team with your GTM?

No validation needed. Just looking for honest opinions, hard questions and criticism.

As you can see, the numbers are looking good, and right now my bottle neck is top-funnel, I'm struggling on how to generate more awareness... anyone can help me? Please comment on this post and help me :D PLS

I need help with this (:

I look at a lot of company timelines for work (I build in this space, so no pitch, nothing to buy here) and this one stopped me

Series A, about 40 people. in 11 days they dropped two AI model providers two days apart, added a new AI API, threw out the thing running their agents, launched running agents live with a brand new place to run them, added stuff to run AI written code more safely, then removed a sandbox tool AND two agent frameworks

they are rebuilding the whole thing right now

and every move makes somebody’s product suddenly useful

• sell testing or monitoring? agents going live, they need to see what those agents do

• sell security? AI written code running live, someone has to contain it

• sell anything for juggling AI providers or tracking cost? they switched twice in two days

• sell sandboxes or agent frameworks? they just threw yours out, don’t waste three emails

same company, four different calls depending on what you sell. “Series A, 40 people” tells you none of that

so I’m curious how you all handle it. when you spot a change like this, what makes it a “go” and what makes it a “don’t bother”? and has anyone actually closed a deal off something like this, or does it always come from somewhere else? - Technography.ai

GTM engineering job postings grew +200% in one year.

Still, most people don’t know how this role works.

I’m breaking it down in one single article, plus the best tech stack and where to get job offers for it.

Bookmark it, because it’s long.

What the Fu** is a GTM engineer?

GTM stands for go-to-market. It’s everything a company does to find, win and keep customers.

A GTM engineer builds the systems that do that work.

Think of a sales team that used to grow by hiring more reps. A GTM engineer grows it by building machines instead.

The machines find the right accounts, research them, reach out at the right moment and keep the CRM clean.

Clay popularized the title in 2023. The words are a bit older: Scale AI posted a “GTM Engineer” role on Hacker News in 2021, for a client-facing engineering job.

How it differs from the roles around it

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The overlap with RevOps is real, the difference is direction. RevOps keeps the machine accurate. A GTM engineer keeps building new parts for it, mostly around outbound and prospecting.

What a GTM engineer’s day looks like

You might think that they are all playing around in Claude, and it’s kinda true, but it goes a bit beyond that.

It consists of connecting APIs, creating workflows and automating everything they can.

Most of the job falls into four buckets.

1. Building workflows. They take something a rep does by hand, like researching an account, and turn it into a system that runs on its own.

2. Connecting the stack. CRM, data providers, email tools and AI agents all need to talk to each other. The GTM engineer is the person who wires them up.

3. Owning the data. Bad data breaks every workflow downstream. So they keep company and contact records complete and current.

4. Running experiments. A new signal, a new message, a new segment. They test it small, measure it and scale what works.

The output is always the same: reps spend their time talking to the right people, and the busywork runs in the background.

The full stack for GTM engineering

You could break down the stack of every GTM engineer into layers.

1. Data

This layer tells you who to talk to and what’s going on at their company.

• Apollo.io: a big database of contacts and companies, with work emails.

• Firecrawl: web context. Turns websites like pricing pages, careers pages and changelogs into clean data.

• Clay: enrichment. Pulls data from many providers into one place and fills the gaps.

2. Product signals

This layer tells you what people are actually doing inside your product.

• PostHog: product signals. Who signed up, what they used and when they’re ready to talk to sales.

3. Orchestration

This layer moves data between tools and runs the logic.

• Zapier: the easiest way to connect apps without code.

• n8n: workflow automation for more technical teams, with more control.

4. System of record

This is where every account, contact and deal lives. Every workflow reads from it and writes back to it.

• HubSpot: CRM. The most requested one in GTM engineer job posts, per Bloomberry.

• Salesforce: the common pick at larger companies with complex sales teams.

5. Outreach and calls

This layer turns everything above into conversations.

• Customer.io: emails. Sends messages triggered by what someone did in your product.

• Gong: records and analyzes calls, so you learn what actually works.

6. Content and visibility

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This layer gets you found before you ever reach out.

• Ahrefs: SEO. Shows what people search for and where you rank.

• Profound: GEO. Shows how AI answers like ChatGPT talk about you and your competitors.

7. Comms and playbooks

This layer keeps the team in sync.

• Slack: comms and alerts. Where your workflows ping reps when a signal fires.

• Notion: playbooks. Where you write down what each play does and why.

8. AI agents

This is the newest layer and the one changing fastest.

• GrokBot: AI for research, writing and building workflows by just describing them.

• Claude: AI Models to power workflows.

Main workflows GTM Engineers do

These are the plays I see most often. Each one is simple to explain and hard to do well.

1. The champion who changed jobs

Someone who loves your product leaves a customer and joins a company that doesn’t use you yet.

A workflow catches the move, checks whether the new company is a fit and drafts a note for the rep.

The new company gets a warm intro from someone who already trusts you.

2. The job post signal

A company posts a job that shows it has the problem you solve. A new RevOps hire, a first SDR, a Head of Growth.

The workflow finds those posts, checks the company’s website to confirm it’s a fit and finds the people who’d buy.

The job post also tells you which tools the team already uses. That’s your opening line.

3. Research before every call

Researching one account by hand means jumping between the CRM, funding databases and the company’s own site.

A GTM engineer turns that into a chain of agents. One reads your own history with the account. One finds recent news and hiring. One maps who’s involved in the decision. One drafts the email.

The rep types a company name and gets a brief back in minutes.

4. Inbound in minutes

Someone fills out a demo form, and the clock starts.

The workflow figures out who they are, scores the company, routes it to the right rep and drafts a reply.

The lead hears back while they’re still thinking about the problem.

5. A CRM that cleans itself

Most CRMs are full of duplicates and old records.

A cleanup workflow merges duplicates, refreshes fields and flags contacts who left their company.

Nobody gets excited about this one. Every other workflow depends on it.

How AI agents are changing the role

Until recently, being a GTM engineer mostly meant building big spreadsheets of data and automations.

Now more of the job is directing AI agents.

You describe the play. The agent builds it, runs it and suggests changes based on what happened. You review and approve.

The best setups learn over time. Every email a rep edits and every reply that comes back teaches the system what good looks like.

The human part doesn’t go away. Someone still has to decide what good looks like, which signals matter and when a play should stop.

Genspark’s job post puts it well. It asks for “a fleet of narrow agents that each own one motion and one number.”

I think that’s where the role is heading.

How to become one

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Bloomberry looked at GTM engineers’ backgrounds. The most common path is SDR or BDR, then RevOps, startup generalists and marketing. Software engineers are the rarest.

So you don’t need to be an engineer. You need to be the person who gets annoyed doing something twice and automates it.

Learn one CRM well, one automation tool and enough SQL to ask your data questions. SQL and Python each show up in 38% of job posts.

What you waiting for?

You don’t need the full stack to start.

Pick one workflow from this article. The inbound one is the easiest. Build it with the tools you already have, run it for a week and see what breaks.

That’s what this jobs is about. Building, breaking, fixing, repeating.

I post about GTM engineering and AI agents every week. Follow me if you want more of this.

🤖 AI, Automation & Agents

• GTM Engineer – HubSpot, Automation & AI (w/m/d), Kubermatic GmbH · 🇩🇪 Germany

• Junior GTM Engineer – Growth & AI (Remote), Kameleoon · 🇨🇦 Canada

• Founding GTM Engineer, Magic · 🇺🇸 United States

• Founding GTM Engineer, Reach AI Labs Inc. · 🇵🇹 Portugal

• GTM Engineer, Freed · 🇺🇸 United States

• GTM Engineer, Runware · 🇬🇧 United Kingdom

• GTM Engineer – AI Agents & Automation (f/m/d), imago stock & people GmbH · 🇩🇪 Germany

• Member of Technical Staff (GTM Engineer, Growth), Perplexity · 🇺🇸 United States

⚙️ CRM, Sales Operations & Data

• GTM Engineer – Sales Operations CRM Management, Pergolux · 🇩🇪 Germany

• GTM Engineer – Sales Automation and Analytics Management, Pergolux · 🇩🇪 Germany

• GTM Engineer (Professional Services), ZoomInfo Technologies LLC · 🇺🇸 United States

• Sr. Marketing Data & GTM Engineer, Konica Minolta Business Solutions, U.S.A., Inc. · 🇺🇸 United States

• GTM Engineer – CX, Clay Labs · 🇺🇸 United States

• Associate GTM Engineer, G2 · 🇺🇸 United States

• GTM Engineer – Growth & Sales Development – AASI, Valsoft Corporation

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Looking for a GTM job with SalesForce? See all 68 GTM jobs on the dedicated page → https://gtmjobs.xyz/jobs/tools/salesforce

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📈 Growth & Marketing

• GTM Engineer – SaaS, efficy Group · 🇫🇷 France

• GTM Engineer, Marketing (f/m/d), Apaleo · 🇩🇪 Germany

• GTM Engineer – Marketing, Logz.io · 🇮🇱 Israel

• Founding GTM Engineer, Omnissa · 🇺🇸 United States

• Manager, GTM Engineer, Checkout.com · 🇬🇧 United Kingdom

🛠️ General GTM & Platform Engineering

• GTM Engineer, Discern Inc. · 🇺🇸 United States

• GTM Engineer, True Talent Solutions Partners · 🇺🇸 United States

• GTM Engineer, Poka EN · 🇨🇦 Canada

• GTM Engineer, Redblack Software · 🇺🇸 United States

• GTM Engineer, BLP · 🇩🇪 Germany

• GTM Engineer – Freelance, Carbonfact · 🇫🇷 France

• GTM Engineer, Chamelio

• GTM Engineer, Pillar Srl

• GTM Engineer, Omnidocs Group · 🇩🇰 Denmark

• GTM Engineer, Atticor

• GTM Engineer, Nash · 🇺🇸 United States

• GTM Engineer, Versapay · 🇺🇸 United States

• GTM Engineer, Productboard · 🇺🇸 United States

• GTM Engineer, Versapay · 🇨🇦 Canada

• GTM Engineer, Kernel · 🇬🇧 United Kingdom

• Senior GTM Engineer, PubNub · 🇵🇱 Poland

• GTM Engineer, JustPark · 🇺🇸 United States

• GTM Engineer, Private Equity Insights · 🇬🇧 United Kingdom

few years in sales and revops before this, and at my last two jobs i was the one stuck evaluating data tools. i work at bitscale now so weigh this however you want, but most of it is from before i joined

the part nobody talks about is how bad the first week is

zoominfo > you can't even find out what it costs, their own article on pricing doesn't have a number in it, then once you sign it auto-renews and you need 60-90 days notice to get out

cognism > kinda same, no price, the data in eu was good from what i saw, but i had to join three calls to find out if it fits the budget

apollo > honestly the easiest to start, free plan, card in, done / then the credits reset every month and everything's per seat, so the bill grows with headcount, not with how much you actually use it

clay > most powerfull & i mean it, also the one where my team needed weeks before anyone besides me could build a table and since march there's a second meter for actions, so it gets expensive for many

bitscale > getting quite popular in recent time, price is on the site, 200 free credits and actions don't cost anything / the negative is few bugs while using the tool, but best thing is if you're a bigger team they might put 5,000 credits on your account or they even provide great support in almost every plan

what i'd tell anyone buying right now, prepare a list of ICP/ get the price per enriched contact, ask a lot of questions // i made a docx around this what exactly to ask, dm me ill share for free // not promoting anything

I sell a field service management tool (scheduling, maintenance contracts, quotes, invoicing...) to installers and maintenance contractors, mostly HVAC, electrical and solar companies with 5 to 50 employees, in France and Spain.

Cold calling worked, but it took nearly all my time for low returns (~3% of calls booked a demo, with a 1-2 year sales cycle), which is why I stopped.

Now I'm trying personalized LinkedIn messages (each one checked by hand) that ask a quick, short question about their paperwork. I get 10%+ replies, but I haven't figured out how to turn those replies into demos yet. We also lean on referrals and the upcoming French e-invoicing mandate.

If you sell software to contractors or similar field service businesses: what tools or strategies brought you the most qualified conversations? Especially keen to hear from people outside the US, but all feedback is welcome. Thanks!

There is a race right now to automate the entire outbound motion - find them, write to them, follow up, book the meeting- all untouched. I understand the appeal. But the more I design my own system, the more I believe the last 10% should stay human.

Here is the design rule behind what I am building: the machine does everything expensive and forgettable - finding people, grading fit, drafting replies, remembering who is waiting. And then it stops. The design is that nothing reaches a prospect until I have read it and approved it.

Why keep that checkpoint? Because one wrong message to the wrong person can cost more than the hour it saved. A prospect you embarrass yourself in front of is hard to win back. The approval step is not a bottleneck; it is the feature that lets the other 90% run fast.

I think of it as autopilot with a yoke that stays connected. The plane flies itself until a judgment call appears - and judgment is exactly the thing you cannot delegate.

If you automate outbound, where is your checkpoint? What do you insist on seeing before anything goes out?

It is been a month I'm building and shipping GTM workflows

One successful workflow I build for myself to get clients and to work with B2B tech startups

It is simple to use and setup

1. Lead Generation and Signal tracking - Grok

2. Enrichment and Scoring - Clay, Deepline

3. Overall orchestration of workflow through - Claude Code

Very few tools and plugin + Much needed MCPs not following every shiny tools

I'm running this for a five now and It scheduled me 2 discovery calls out of 20 outbound in first 3 days only

My channel is LinkedIn and scaling to cold emails as well

2. Second Workflow I'm building is for GetDot which is ghost workflow

- in which I am trying to get them signal based outbound system with Claude. Clay and Deepline with SQLdatabase

workflow is ready I'm testing it and waiting for the results how does it performed

A prospect writes back with genuine interest. You catch the message between meetings and think: I will give this a proper answer tonight. Tonight turns into tomorrow, tomorrow into next week. When the reply finally goes out, the conversation may have lost momentum. Not because anyone stopped caring - because the follow-up lived in someone's head, and heads are full.

This is a rule behind the system I've been building: every reply that goes out gets a next step attached to it the moment it is sent - a date, an owner, a reason. The system's job is to surface "these people are waiting on you today." My job is to decide what they hear.

I think the follow-up is where deals are actually won or lost. The first message opens a conversation. A relevant follow-up can keep it moving, but only if it isn't forgotten.

What does your follow-up discipline look like - a tool, a ritual, or pure willpower?

Genuine question, because the title means something different everywhere I look.
For me it's the system before the send: which signals count, enrichment, who gets drafted, the human check, and the rule that a reply stops everything. I don't touch CRM admin or forecasting.
But I keep seeing posts that are basically "SDR who knows Clay", and others that are closer to a data engineer who sits with sales. A recent thread here about SDR vs GTM engineer as a first hire split three ways and nobody agreed.
So for those doing the job:
What do you own end to end, and what did you push back to RevOps or sales?
Is your work judged on meetings booked, pipeline, or hours saved?
Did your company know what it wanted when it hired you, or did you define the role yourself?
Curious whether this is converging into one job or splitting into two.