The hard problems aren't the screens. They're resolving and classifying messy company data at scale, modelling supply chains you can't directly see, and making every output explainable enough to stand up to an auditor.

This role is for someone who simply loves building things, and still gets a kick out of shipping something hard and seeing it work. You'll spend your days hands-on, designing, coding and solving problems, not managing people or sitting in meetings. This isn't a stepping stone to an engineering manager or CTO, and that's deliberate. Growth here means bigger problems, more ownership and more technical influence over time, along a senior IC path toward Lead or Principal.

That's what AI-first development makes possible. One great senior engineer working with modern AI tooling can now have the kind of impact that used to take a small team. We want people who are energised by that, not people looking for a team to run.

About this opportunityWe're a startup with a small engineering team with blue-chip customers who renew because regulation now requires them to answer questions that they don’t have answers for. That means real demand, real data, and real consequences for getting it right.

As our Senior Software Engineer, you'll own complex, data-heavy features end to end, across our Next.js/TypeScript stack and down into the data that powers it. You'll take problems from rough idea to production, shaping the technical approach, making the calls on architecture and trade-offs, and standing behind the quality of what ships. In a team this size, your technical judgement shapes how the platform evolves, not just the features you build.

We build AI-first, on a modern stackThat starts with how we work: AI coding agents, custom harnesses and eval and QA tooling are part of how we write, review and ship code every day, and if you'd be happy to never go back to writing code by hand, you'll fit right in. But it goes well beyond coding. Our platform runs on the kind of stack most engineers only read about:

• Graph databases modelling global supply chains
• Agentic workflows running in production
• AI evals and tracing to keep model outputs measurable and trustworthy
• Structured data models that turn unstructured inputs into audit-grade results
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