GTM Engineer

GTM Engineer - AI Native Scale Up 📍 Location: London, 4 days a week in the office (WFH Wednesdays) 💸 Salary: £70,000–£100,000 + equity 📈 Stage: Early stage and moving fast, live product with paying customers, an enterprise pilot going live within weeks ⚙️ Technical Environment: Python, TypeScript, LLMs and agentic workflows, Claude and Claude Code We're partnered with a fast growing AI native company as they build out their go to market engine from scratch. The product itself is a genuine step change for a traditionally slow moving, relationship driven UK industry. It's live, it's generating revenue, and adoption numbers are strong. This is a real AI colleague that customers are learning to trust and rely on daily. The role is shaped as an engineering seat pointed directly at revenue. You'll be building the systems the commercial team runs on, starting with the data and automation behind sales, then extending into marketing and beyond as the business scales. This is all about building AI backed solutions to support the GTM function of the business. You'll sit next to the people who use what you build, adopting your solutions to their needs. Watch the real workflow, shipping and iterating as you learn user behaviour, then harden what earns its place. LLMs are the default tool . This is a small, AI augmented team already running much of its week through agentic workflows, and you'll be working in Claude and Claude Code daily, wiring tools together over MCP and APIs, writing code where code is the right answer. Requirements Ability to write production code in Python, TypeScript or similar - you define what is built and in the appropriate language. You're comfortable with APIs and data, and able to query a database yourself 2-5 years of experience, from any background (engineering, technical product, data, consulting, startup or your own projects) as long as the skills are there A strong technical degree (engineering, maths, computer science, physics or similar) from a Russell Group university or international equivalent LLMs in everything you do, and something built well beyond a prompt: agents, tool use, retrieval, evaluation or a workflow that runs unattended Commercial instinct: you know a funnel, and can tell when a metric moving actually matters