Senior Applied AI Engineer, Product

Fresha is the leading AI-powered operating system for the global beauty and wellness industry, giving businesses everything they need to run smarter, grow faster and build stronger client relationships.

In May 2026, Fresha secured an $80M growth investment from KKR at a valuation of $1B
, officially reaching unicorn status and bringing total funding raised to $285M. Already profitable, Fresha is using this investment to accelerate global expansion and drive the next generation of AI and product innovation.

Today, Fresha operates at global scale, with offices across Europe, North America, the Middle East and Asia Pacific. Our platform is trusted by 130,000+ beauty and wellness businesses across 120 countries
, with more than 1.5 billion appointments booked to date.

Our mission is simple:
help beauty and wellness businesses grow by giving them the technology they need to attract clients, manage their operations and run their business in one place.

We’re continuing to scale rapidly across markets, teams and products, making this an exciting time to join us.

Senior Applied AI Engineer | The Bower, 207-211, Old St, Tower, London EC1V 9NR
| Four days in office, Wednesdays remote

You’ll join a cross-functional product team and report to its Team Lead. The team builds AI products that Fresha's partners and their clients use directly: today that means an AI receptionist that answers a salon's phone, books appointments and hands off to a human when it should, and the support tooling around it.

This is a hybrid role: half senior backend engineer, half applied AI engineer. The AI is only as good as the system around it. A model that gives a great answer is no use if the booking it promises doesn't exist, the call drops when a downstream service is slow, or nobody can tell whether last week's prompt change made things better or worse.

You’ll own both halves.

We work on a short feedback loop. Your change usually reaches production the same day you write it, behind a feature flag. The team that builds a service looks after it, including a shared on‑call rota. Everyone on the team works across the whole stack; there's no “AI person” and “backend person”.

Every engineer gets Claude, Codex or Cursor from day one, and people build their own agent workflows rather than being handed one. We look for engineers who know how to break work up, how to review what comes back, and how to stand behind the result.

• Own AI features end to end: Design it, build it, evaluate it, ship it, and look after it once real customers are talking to it.

• Build the system around the model: The services, tools and integrations an agent needs to do real work: checking availability, making bookings, taking payments, escalating to a human. Correct when it retries, safe when it's wrong.

• Prove it works: Build the evals, graders and production checks that tell us whether a change made the product better. Treat them as part of the product, with their own accuracy you measure and improve.

• Engineer the context: Decide what the model knows, what tools it has and how they're described. Most quality gains come from here, not from the prompt.

• Work in real time: Voice and chat run on tight latency budgets. You’ll balance model choice, cost, latency and quality, and keep all four in view.

• Learn from real conversations: Read transcripts, instrument runs end to end (traces, cost, latency, outcomes), and turn what you find into the next change.

• Work with the rest of the team: Product, Design, Support and other engineering teams, from the first conversation through to release.

• Have a say in how we build: Our architecture, our AI practices and our engineering practices aren’t fixed.…