Orbital is the AI platform for real estate legal work.

Real estate shapes the world around us: our homes, our workplaces and our cultural landmarks. It’s the infrastructure that powers everyday life and is the world's largest asset class. Yet behind every single deal, real estate and legal teams work hard to navigate decades-old complexity; manually connecting documents, hidden obligations, geo-spatial context, and multiple parties into a path forward. We connect the legal and physical record, so that path is clearer than ever before. Our mission is to make real estate transactions faster, clearer, and less manual.

Here, you'll be part of changing how the deals behind the largest asset class get transacted, and will help shape the future of real estate.

A bit about us

🚀 We’re building at the forefront of AI technology, without losing the deeply human expertise our clients depend on.

🏢 Orbital is built with former practising real estate lawyers and 250+ years of combined legal expertise. We're purpose-built for the complexity of real estate: accelerating due diligence, drafting and negotiation, and deal coordination with legal-grade precision.

💰 We've just raised a $60m Series B, led by Brighton Park Capital, to accelerate our global expansion – $75m raised to date.

🤝 We're trusted by 5,000+ real estate professionals, spanning UK Magic Circle and US Am Law 100 firms, to household names like M&S. Leading firms like BCLP, Orrick, Goodwin, Womble Bond Dickinson and Clifford Chance rely on Orbital to remove the busywork, freeing legal and real estate teams to focus on sharp judgment, standout client service, and closing deals faster.

💡 Working at Orbital means joining a bold, ambitious team, with the trust to take ownership and make a real impact from day one.

The Role 🔭

We're rebuilding Orbital's go-to-market around AI. Not by adding another tool to the stack, but by rethinking how we find, win and grow customers from first principles.

As an AI Ops Engineer (GTM) on our Revenue team, you'll sit with the people doing the work across Sales, Marketing, Customer Success, Product and Finance. You'll question how we work today, reimagine it from first principles with AI, and build what replaces it. You're essentially an internal forward deployed engineer. Over time, you'll help join up the whole business so every team works off the same data and foundations.

The technology is changing every month. We want someone who finds new and unusual ways of doing things, and builds them so they keep working as the tools change.

You'll report to the VP of RevOps. This role is for someone early in their career with exceptional raw ability: we care far more about how fast you learn and what you've built than about years of experience or job titles.

What you'll do 🎯

Embed with revenue teams to understand how they work, and spot where AI and automation could change the outcome

Build AI agents, workflows and integrations that remove manual work and help teams make better decisions

Connect our systems (CRM, marketing, CS, product and finance data) so teams work from one trusted picture of the customer

Try new approaches, test them quickly with real users, and keep what works

Ship in short cycles and prove impact. Build the measures that show pipeline created, conversion lift and time saved

Build things to last: documented, maintainable and easy to adapt as models and tools change

Share what you learn and help colleagues get more out of AI

What you'll bring 💡

A strong academic background in a quantitative or technical subject (for example computer science, engineering, maths, physics or economics), or equivalent evidence of exceptional ability

Clear evidence that you build: side projects, tools, automations, a GitHub, a startup, hackathons, or things you've shipped at work

Hands-on experience building with AI, such as LLM APIs, agents or AI coding tools

Some coding ability (for example Python, JavaScript or SQL) and comfort working with APIs

Exceptional problem-solving: you break messy problems down, learn new things quickly and explain your thinking simply