How to Become a GTM Engineer, Starting From Zero

A job that barely existed three years ago

As of September 2026, almost 3,000 people held the job title GTM engineer. One recent dataset counted around 900 unique open roles globally, and 68% of the people calling themselves GTM engineers had entered the profession in the previous 12 months. Among US job postings that disclosed pay, the median advertised salary for individual contributors was around $150,000 a year, and lead and principal roles were around $210,000.

GTM stands for go-to-market. I think this role is one of the best examples of what is actually happening in the AI era. AI has removed some jobs, but it is not destroying all of them. It is also creating hybrid roles that would not have made sense five or ten years ago, and GTM engineering sits right in the middle of sales, marketing, data, software and AI. I covered another of these hybrid roles in how to become a forward deployed engineer.

First principles: every business needs customers

Every business has the same fundamental problem: how do I get customers? You can build the best product in the world and still need to find the right audience, reach them, earn their trust, close them and keep them coming back. That whole machine is your go-to-market system.

Imagine you sell software to restaurants. Someone has to decide which restaurants to target, find the decision-maker and their email, research the business, judge whether they are qualified and can afford it, write a relevant message, add them to the CRM, contact them, follow up, route replies to the right salesperson, book the meeting, update the CRM and learn from what happened. Traditionally that is spread across sales development reps, marketing ops, revenue ops, salespeople prospecting and developers building integrations.

The GTM engineer asks a simple question: why are humans doing all of this manually? Instead of researching one company, build a system that researches 10,000. Instead of judging every lead by hand, build rules and an AI workflow that scores them. Instead of a salesperson opening ten tabs before a meeting, build something that prepares the account research automatically.

Why this role is suddenly everywhere

This is not popular because someone invented a cool title. Three technologies had to converge. Clay, the company that popularised the term, describes the role as coding revenue rather than coding software, and I think that is a very useful mental model.

Knowing the tools is not the job

A common misunderstanding is that learning Clay, ChatGPT and Zapier makes you a GTM engineer. That is like saying knowing VS Code makes you a developer.

One analysis in July looked at 739 GTM engineering job postings. About 84% mentioned workflow automation, 65% data enrichment and 61% outbound at scale. On the technical side, 86% wanted AI or LLM skills, 66% mentioned APIs and webhooks, 41% wanted Python and 35% SQL. Clay appeared in roughly 60% of postings, HubSpot in about 52% and Salesforce in about 48%. So this is not a purely no-code role, and not a purely coding role either. Companies want someone who understands the business problem well enough to build the system around it.

The three skill buckets

Commercial thinking. Understand ideal customer profiles, positioning, offers, funnels, why somebody buys, what makes a good or bad lead and how sales actually works. AI cannot save you from a terrible offer.

Systems thinking. Can you look at a messy process and turn it into a streamlined system: an input, some logic, an action, then feedback you iterate on? This is probably one of the most important skills of all, and I talk about it more in why the future belongs to systems thinkers.

Technical execution. APIs, webhooks, databases, CRMs, automation tools like n8n or Make, a sales tool like Clay, basic Python and SQL, and increasingly AI agents and structured LLM workflows. You do not need to be an elite software engineer, but you need to be technical enough not to be limited to clicking buttons in one SaaS tool.

My rule: automate the work, but never outsource the thinking. AI can research 10,000 companies, but you should understand what makes a company worth researching in the first place. That is where your value comes from.

If I were 22 with no experience: the first three months

I would not spend months or years collecting degrees or certificates, because this space moves too fast. And only around 2% of current GTM engineering openings explicitly ask for juniors. If your plan is to apply to lots of entry-level GTM roles and hope one hits, you will probably struggle. So I would manufacture the experience myself.

Month one: learn GTM without the engineering. Ideal customer profiles, lead generation, inbound versus outbound, qualification, sales funnels and CRM basics. Understand the business before touching automation.

Month two: pick one technical stack. For example HubSpot, Clay and an LLM API, plus basic SQL, basic Python, and a proper understanding of APIs and webhooks. Do not learn 25 tools. Tools change, the concepts stay the same.

Month three: build AI systems. One that finds companies matching your ICP, enriches their data, researches them with AI, scores them and pushes qualified accounts into a CRM. Another where an inbound lead is enriched, classified, routed to the right salesperson and triggers a Slack alert. Or one that takes a sales call transcript, extracts objections and next steps, updates the CRM and drafts a follow-up email.

Proof beats passion

Once you have built those, you become interesting. Instead of telling an employer you are passionate about AI and have tried ChatGPT, you can say: here are the systems I built, the architecture, the tools, what it costs to run, how I handled failures, the business outcome, and how it could apply to your business. Right now, proof is worth more than almost anything else.

Then get reps quickly. Take a startup, a friend's business, your current company or a small local business, find one painful manual revenue process and automate it. This is how I think about operations at Sigmaschool too. I do not want anyone manually moving leads around forever. Where did the lead come from? Are they qualified? Who follows up, and how fast? What happens if they do not reply? What should management see?

One warning. Automation does not fix bad operations, it scales them. If your qualification logic is wrong and you automate it, you now make wrong decisions ten times faster. Business judgement still matters enormously.

GTM yourself when you apply

This profession is about go-to-market, so go to market on yourself. Build a shortlist of companies, research them, and find the people running revenue, growth or ops. Send a two-minute Loom showing something you built, or better, the GTM workflow you would build for their business. That instantly separates you from thousands of people clicking Easy Apply on LinkedIn.

Do not only search for "GTM engineer". The title is still forming. Try GTM systems engineer, growth engineer, revenue automation, RevOps engineer, AI operations or marketing engineer. About 54% of identified GTM engineering openings are in the US, so if you are in Malaysia, do not wait for every local company to discover the exact title. Companies usually discover the problem before they know what to call the person who solves it. Our guide to AI jobs in Malaysia covers the local picture.

What this tells you about where work is going

I do not think everyone reading this should become a GTM engineer. What the role shows is where the scope of work is heading. The most valuable person in a company is increasingly not the one who executes one task well, but the one who understands the business problem and the technology and can build a system that solves it repeatedly. That applies to GTM engineers, forward deployed engineers, AI engineers, product people and founders, and to plenty of jobs that have not been named yet.

So do not start by asking which course to take. Ask what revenue problem you can solve and whether you can build a system that solves it. Build it, document it, get someone to use it, then build another. The technical layer, APIs, databases, Python and AI workflows, is exactly what our AI-Native Software Development Programme teaches from the fundamentals up. If you want to try that kind of work first, start with the free trial.