TL;DR
- If you want to move faster, work smarter, and scale revenue with precision, a GTM Engineer could be your organization’s most powerful lever.
- This role is gaining traction fast in AI-native startups, sales-led orgs, and PLG SaaS businesses, where personalization, automation, and data orchestration are mission-critical.
- Ideal candidates have a mix of GTM experience, RevTech fluency, programming ability, and AI agent proficiency.
- Core responsibilities include: deploying AI agents, automating workflows, integrating GTM systems, and modeling full-funnel analytics.
The next wave of GTM efficiency will come from systems that integrate AI agents into every function, activity, and workflow. The GTM Engineer–a hybrid role at the intersection of revenue strategy, automation, and AI operations–will be the architect of those systems, and whether you know it or not, you should probably start looking to hire one.
Since the role is still nascent, there are many competing interpretations of what a GTM Engineer should do. Some organizations position the GTM Engineer as a new kind of sales rep, working with customers to implement AI tools and helping them build more complex, automated workflows. Others envision the GTM Engineer as a specialist within RevOps, responsible for deploying AI-driven agents in GTM workflows (think lead qualification, content creation, rep onboarding, etc.). For the purposes of this discussion, we’ll focus our attention on this latter definition of the role.
What does a GTM Engineer actually do?
GTM Engineers build the connective tissue of modern revenue systems, designing the AI infrastructure that aligns sales, marketing, and customer success around a shared engine of growth. Although their principal responsibilities continue to evolve, today, they often center around testing and deploying agents to automate tedious tasks.
The exact scope of a GTM Engineer’s role will vary significantly based on their company’s needs and organizational maturity. At some companies, the role might focus exclusively on deploying and fine-tuning AI agents, while in others, it may expand into adjacent areas (e.g., data management) on which effective AI implementation depends.
Common responsibilities include some combination of:
- AI automation and agent deployment: embedding AI agents to autonomously execute critical workflows such as lead qualification, personalized outreach, and customer onboarding
- System integration and design: ensuring AI agents seamlessly interact with existing platforms (CRMs, CDPs, analytics tools) such as Salesforce, Segment, and Amplitude to operate effectively
- Experimentation frameworks: setting up infrastructure to rigorously test AI-driven workflows, continually optimizing performance
Ultimately, the differentiator between a GTM Engineer and traditional RevOps roles is the primary focus on AI. Unlike RevOps, which largely optimizes existing processes, GTM Engineers proactively architect and manage the infrastructure that enables intelligent systems to independently execute, and continuously improve, GTM workflows.
Skills and experience: What makes a great GTM Engineer?
While the exact tech stack may vary, here are some of the foundational competencies of today’s most effective GTM Engineers:
| Skill area | Details |
| GTM experience | Understands funnel metrics, buying journeys, and commercial strategy across marketing, sales, and CS |
| RevTech fluency | Possesses a deep understanding of CRMs, marketing and sales tools, and CS platforms |
| Agent development | Proficient at prompting; understands agent-creation best practices |
| AI integration | Has experience with APIs for ChatGPT, Claude, Gemini, and/or agent orchestration platforms (n8n, Copy.ai, Make). |
| Programming & scripting | Proficient in Python, SQL, and/or JavaScript. Can build lightweight apps or workflows |
| Data engineering lite | Familiar with tools like Databricks, Snowflake, or Fivetran; can extract data from data warehouses |
Who should the GTM Engineer report to?
Today, the GTM Engineer often reports into RevOps. However, in forward-thinking orgs, they are increasingly being embedded directly in growth, marketing ops, or product-led sales teams.
The best orgs treat GTM Engineers not as support staff but as strategic operators who:
- Own the agentic workforce
- Lead AI workflow orchestration
- Prototype new revenue motions
- Enable human and agent collaboration
Who’s hiring for GTM Engineers?
The discussion around GTM AI has led to a surge in demand for “GTM Engineers,” “Revenue Engineers,” “Growth Engineers,” and “GTM Developers,” particularly among:
- PLG SaaS companies like Notion, Retool, and Ramp
- AI-native startups embedding agents into customer workflows
- Mid-market SaaS with complex sales motions and data needs
- Sales-led orgs scaling personalization and automation across global GTM teams
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(Below is a sample job description for a GTM Engineer. Modify to fit your organization’s stage and needs.)
Job description: GTM Engineer
Title: GTM Engineer
Location: Remote / Hybrid
Reports to: Head of Revenue Operations or VP of Growth
Level: Senior IC or Manager
About the role
We’re hiring a GTM Engineer to help us scale our revenue engine through automation. You’ll sit at the center of our sales, marketing, and customer success efforts, designing the systems that enable speed, precision, and AI-powered execution across the funnel.
Key responsibilities
- Design and automate AI-driven GTM processes across CRM, marketing automation, and outreach platforms, leveraging AI-powered workflows to enable scalable demand generation and pipeline acceleration
- Deploy and optimize AI agents to automate tasks across prospecting, onboarding, and account management
- Build experimentation frameworks for outbound sequences, onboarding flows, and lifecycle messaging
- Model GTM data to surface insights, forecast revenue, and enable better decision-making
- Collaborate with RevOps, Growth, and Product teams to align GTM workflows with overall tech stack and business strategy
Qualifications
- 2-5+ years in Revenue Ops, Growth, or Solutions Engineering with a focus on pipeline generation and system integration
- Experience with building agentic AI systems (e.g., GPT, Claude, Gemini) and integrating AI/LLM-powered workflows into GTM teams (e.g., using Copy.ai, ChatGPT, Jasper, Claude, etc.)
- Strong comfort with APIs, webhooks, and data workflows (e.g., Zapier, n8n, Make, Retool, or custom Python scripts)
- Experience with GTM tools like Salesforce, Outreach, Segment, and LLM platforms
- Deep understanding of B2B SaaS funnels and metrics (MQLs, PQLs, ACV, CAC, LTV)
- Strong communication skills (i.e., you can translate technical solutions into business impact)
Comfort with a scrappy, fast-paced, test-and-iterate culture; able to turn ideas into prototypes quickly and passionate about experimenting with new tech