GTM Engineer

Remote from: USA
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 16 Jun 2026
Apply before: 16 Jul 2026
Experience level: Midweight
Views / Applies: 11 / 4

About company

About Pogo

Verified consumer research powered by AI.

Computer Software
2021

Actively Hiring

AI Summary

This GTM Engineer role at Pogo is a unique blend of builder and operator, tasked with creating AI-powered systems to drive revenue. You'll partner with Growth, Sales, Design, and Engineering teams to ship code that directly impacts pipeline and conversion. The position requires deep experience with modern GTM tools (Clay, HubSpot, etc.) and LLMs, as well as a product-minded approach to internal tooling. As the first GTM engineer, you'll shape the function from the ground up, working in a fast-paced, ambiguous environment where weekly shipping is the norm.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role demands a rare combination of software engineering, AI/LLM expertise, and GTM operational knowledge, plus the ability to thrive in ambiguity and fast iteration cycles.

Salary Analysis

AI Insight The job post does not specify salary, but for a first GTM Engineer at an early-stage AI startup in the US, the total compensation likely includes significant equity. Base salary is estimated between $120k-$200k, with a median around $140k, reflecting the senior individual contributor level and the startup's stage.

Key Skills

GTM Engineering AI/LLMs Sales Engineering Growth Engineering HubSpot Clay API Integration Revenue Operations Automation Internal Tools

Cover Letter Sample

I am excited to apply for the GTM Engineer role at Pogo. My background combines hands-on software engineering with a deep understanding of go-to-market systems, and I have a proven track record of building AI workflows that directly impact revenue. At my previous company, I integrated LLM-powered agents into the sales process, resulting in a 30% increase in lead conversion.

I am particularly drawn to the opportunity to be the first GTM engineer and shape the function from the start. I thrive in fast-paced, ambiguous environments where I can build internal tools that turn messy processes into automated systems. My experience with tools like Clay, HubSpot, and OpenAI APIs allows me to hit the ground running.

I am also a strong communicator comfortable working across engineering, sales, and growth teams to identify leverage points and ship solutions weekly. I look forward to the chance to discuss how I can contribute to Pogo's growth.

Possible Interview Questions

Describe a time you built an AI-powered tool that directly impacted a revenue metric. What was the metric, and how did you measure success?

At my previous job, I built an AI agent that automated personalized outbound emails based on prospect behavior. We measured success by the increase in reply rates and demo bookings. The tool improved reply rates by 25% and contributed to a 15% lift in pipeline generation within three months.

How would you audit and improve a GTM stack that includes HubSpot, Clay, and Apollo? Walk us through your process.

I'd start by mapping the data flow and identifying manual handoffs or bottlenecks. Then I'd evaluate each tool's API capabilities and integration points. For example, I might find that lead enrichment is done manually; I'd automate it via Clay's webhooks and trigger actions in HubSpot. I'd also look for redundancy—if both Apollo and Clay are used for enrichment, I'd consolidate and build a unified pipeline.

You discover that sales reps are spending 2 hours per day on account research. How would you automate this using LLMs and your GTM stack?

I'd build an AI agent that takes a list of accounts, scrapes recent news, tech stack, and key contacts using tools like Apollo and public APIs, then generates a concise research brief. The agent would use a language model to summarize and highlight key insights. The output would be pushed to a HubSpot custom object or Slack notification, cutting the research time to minutes.

Tell us about a time you had to choose between building a custom solution and using an off-the-shelf tool. What influenced your decision?

At a startup, we needed lead routing logic. Off-the-shelf tools were expensive and inflexible, so I built a lightweight internal tool using a low-code platform. However, later as requirements grew complex (e.g., multi-stage scoring), we migrated to a dedicated routing tool. The decision hinged on speed vs. scalability—initially building was faster, but eventually off-the-shelf saved engineering time.

How do you stay updated with the latest AI and GTM tools? Give an example of a recent tool you adopted and the impact it had.

I follow AI newsletters and GitHub repos. Recently, I adopted Cursor for AI-assisted coding, which sped up my development of internal scripts by 40%. For GTM, I started using Cargo for better data enrichment pipelines, which improved our lead scoring accuracy by 20% in the first month.

About your role 👀

As the first GTM Engineer at Pogo, you’ll partner with our Growth, Sales, Design, and Engineering teams to build AI-powered systems that compound revenue. You’ll work on our enterprise AI SaaS product as both a builder and an operator: shipping code that drives pipeline and conversion.

Specifically, you will:

Ship AI-powered workflows that scale our growth and sales motions as a competitive advantage
Audit our existing GTM stack, identify the gaps and redundancies, and start shipping against them fast
Embed with our sales and growth teams to turn their best plays into systems the whole org can run on
Build internal tools and agents that replace manual workflows – lead routing, enrichment, outbound personalization, account research, content distribution, attribution
Know when to reach for off-the-shelf tools and when to build something custom
Partner closely with engineering and design to test new features, give feedback, and dogfood our own AI capabilities for GTM use cases
Publish playbooks and internal guides so wins propagate across the team

About you 👩‍🚀

Likely 2-5 years of professional software engineering experience, with at least some of that applied directly to GTM, growth, or revenue systems
You’re a heavy user of AI tools like Claude Code, Cursor, or Codex and have shipped non-trivial things with them
You’ve built with LLMs and AI agents – think RAG, evals, tool use, prompt engineering
You’re hands-on with the modern GTM stack (Clay, HubSpot or Salesforce, Apollo, Cargo, Gong, Outreach/Instantly, etc.) and can navigate APIs, webhooks, and integrations fluently
You have strong product and design taste – the internal tools you build should feel as polished as the product
You think in systems and like turning messy operational problems into clean, automated ones
You have real commercial instinct – you can sit in a sales call or read a growth dashboard and immediately see where leverage exists
You communicate clearly across technical and non-technical stakeholders, especially when things are ambiguous
You’ve built and maintained production systems used by real users (internal users count)
Bonus:
- Experience at early-stage startups or building your own products
- You’ve shipped AI agents that moved an actual revenue metric, not just demos
- Background in RevOps, Marketing Ops, or Sales Engineering before moving fully into building

Why you might be excited about this opportunity 🙌

Ship real systems that get real usage and move real numbers – pipeline, conversion, retention
Sit at the center of the company. Partner with Growth, Sales, Design, and Engineering
Use the latest tech. Build with the latest coding tools, LLMs, and GTM platforms
Move fast. Ship weekly and get rapid feedback from the teams using what you build
As the first GTM Engineer at Pogo, you’ll shape what this function looks like at Pogo as we scale

Why you might not be excited about us 👎

Priorities may shift quickly. Oftentimes, we’re tackling very ambiguous problems that don’t have clear-cut answers. While we make calculated bets, we have a strong culture of experimentation and we’ll pivot as necessary based on what we’re seeing in the data and what we’re hearing from our users.
At times, you’ll need to build things in a day: we live and breathe a value called “Calculated Speed”, which involves iterating and shipping weekly, rather than spending months going through complex processes or lengthy builds. Our engineers are some of the best in the world, which means anything you design today may be shipped next week.
We don’t have structured management (yet!). We don’t have the management overhead to provide direction every step of the way and we’re looking for someone that gets energized by being able to run with things.
We expect more than 9 to 5 – raw hours make an impact at our current stage. That said, we trust each team member to create a flexible work schedule that allows them to be most productive while accommodating other priorities outside of work. We also strongly encourage time off to recharge the batteries: in addition to unlimited PTO, we’ve implemented a minimum 20 days vacation policy 🌴

#LI-Remote

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