Founding Software Engineer

About [Employer hidden — sign up to reveal]

[Employer hidden — sign up to reveal] is Europe's fastest-growing company. Number one on the FT1000, number one on the Sifted 100. From £1m to over £100m in under three years, with a small, talent-dense team and an electric culture with day one founder intensity. Now we're aiming for £1bn in the next three. We curate the world's best wellbeing brands across The Four Pillars™: EAT, MOVE, MIND, SLEEP. That's the first chapter. The next chapter is harder and more interesting. We are moving from one market to many, from e-commerce to a technology platform, and from curating wellbeing to defining it. We are a health company, so we think we should act like one. At its fullest expression, [Employer hidden — sign up to reveal] redefines what wellbeing means for tens of millions of people.

Why this role is [Employer hidden — sign up to reveal]

[Employer hidden — sign up to reveal]'s commercial engine runs on manual processes. Pricing changes happen in spreadsheets. Purchase orders are placed by hand. Out-of-stocks are discovered after they happen. Marketing bids are set and left. This works at one market and one speed. [Employer hidden — sign up to reveal] is about to be neither. Project Atlas is taking the company into Germany and beyond. The complexity multiplies: different pricing dynamics, different competitive sets, different supplier economics, all moving simultaneously. Gifted analysts managing this manually in one market drown in five. The answer is not more analysts. It is agentic systems that develop their own commercial logic through continuous interaction with real markets. A system that observes that a particular product category in Germany has different price elasticity than the UK, hypothesises a pricing strategy, tests it, measures the outcome, and updates its own model. A system that gets measurably smarter with every transaction. [Employer hidden — sign up to reveal] is pre-quant. What happened on Wall Street in the 1990s is about to happen in commerce. But this is 2026, not 1995. You are not building rule-based automation. You are building systems that learn. And commerce is a better domain for this than almost any other, because the feedback is fast and unambiguous. Did the reprice increase margin? Did the reorder prevent the stock-out? Did the bid shift improve ROAS? You know within hours. The system can improve at a pace that is impossible in most domains. Nobody else is building this on top of a hundred-million-pound wellness business with half a million regular customers and a multi-market expansion underway. Not a prototype. A production system that trades, learns, and improves. Those systems do not exist at [Employer hidden — sign up to reveal] today. Every one of them needs to be built. That is the job.

What you will own

• The commercial decision engine. Production infrastructure that takes pricing models, demand forecasts, and marketing intelligence and turns them into autonomous action. Repricing. Reordering. Bid adjustment. Inventory allocation. Not a system that suggests. An agentic system that acts, observes, and learns.
• The integration layer. These systems touch everything: e-commerce platform, supplier systems, marketing platforms, competitor data feeds, internal tools. You build the pipelines, APIs, and integrations that let commercial decisions flow through the business in real time.
• The trust architecture. Autonomous commercial systems need to be trustworthy. Monitoring, alerting, human-in-the-loop controls where they matter, audit trails, and the ability to explain why the system did what it did. Trust isn't just a safety mechanism. It's what enables the system to run long enough and consistently enough to get smart. Every hour of reliable operation is an hour of learning. You design that trust.
• The multi-market architecture. Everything works across markets from day one. Different currencies, different pricing logic, different supplier relationships. The architecture absorbs new markets without a rebuild for each one.
• The learning loops. You work directly with the Founding Data Scientist to put models into production and close the feedback cycle. You don't just deploy intelligence. You build the infrastructure that lets it learn from its own decisions.

What you will have built in a year

• At least one commercial decision fully automated in production, learning from outcomes, measurably better than the manual process
• A system architecture designed for multiple markets from the start
• Trust infrastructure that makes the commercial team comfortable letting the system run autonomously
• Data pipelines feeding commercial models with clean, real-time data from every relevant source
• The foundation of a commercial intelligence platform that absorbs new decision types as models are built

Why you're [Employer hidden — sign up to reveal]

You have built systems that make decisions in production. Not dashboards. Not batch reports. Systems where the output is an action: a price change, a bid adjustment, a purchase order. The action happens without a human pressing a button. You know what it takes to make that trustworthy: monitoring, fallbacks, circuit breakers, graceful degradation. You think in learning loops, not just execution. The difference between automation and intelligence is whether the system improves. You care about feedback cycles, outcome measurement, and the infrastructure that turns a system that acts into a system that learns. You think in systems. Not individual services, but how they connect. Data flows, feedback loops, failure modes, scaling bottlenecks. You look at a manual business process and see the intelligent system that should replace it, including the parts that should stay human. You are pragmatic. You build the simplest thing that works. The first version of an autonomous pricing system does not need to be perfect. It needs to be in production, learning, and better than the spreadsheet it replaced. You ship. You are fluent with modern AI tooling and you use it aggressively. You build faster than engineers who don't because you have integrated LLMs and AI development tools into your workflow. You care about the outcome, not the craft of the typing. You have come from somewhere that software makes real-time commercial decisions at scale. Marketplace engineering, adtech, fintech, logistics, trading infrastructure. The domain matters less than the pattern: you have built systems that decide, and ideally systems that learn from their own decisions. You are excited by the blank page. Not optimising an existing system. Building the first version of one. The fact that [Employer hidden — sign up to reveal] has no autonomous commercial systems today is the reason this role exists and the reason it's interesting.

Show us

• A system you built that makes autonomous decisions in production. What does it decide, how fast, and what happens when it is wrong?
• Evidence you have built systems that learn from their own outputs. Not just systems that execute. Systems that improve.
• A moment where you designed for trust. How did you convince people who relied on a manual process to let the system take over?
• Proof you can move fast without moving recklessly. A system you shipped under pressure that was also reliable.
• Something that tells us you care about commerce, not just infrastructure.

The deal

Competitive base plus meaningful equity for the right person. We ask a great deal of the people who work here. We expect full ownership and a genuine commitment to give this chapter everything you have. In return, we will give you the same: everything we have, invested in your growth, your wellbeing, and the defining skills of the next decade. We have built the fastest-growing company in Europe with a team small enough that every person in it shapes the outcome. That is still true today. The next person we hire will change the trajectory of the company. If the most important work of your career is ahead of you, this is the place to do it.

One question

Include your answer in your CV or cover letter attachment when you apply. [Employer hidden — sign up to reveal] currently reprices products manually in spreadsheets. You are building the agentic system that replaces this. On day one you have twelve months of transaction data, competitor pricing scraped daily, and a pricing team who know the products but are drowning in manual work. Describe what you build in the first eight weeks. What does it do, what does it not do yet, how does the pricing team interact with it, and how does it learn? 200 words. Plain English. No decks. Show us how you think.