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Foundation Health is an AI operating system for pharmacy, solving critical workflow problems in specialty pharmacy. The company is built by a team with deep healthcare domain expertise and is backed by experienced leadership (VP of Engineering from DeepMind/Google). The product is live with enterprise customers and the company is actively scaling.
In this role, you will design and build backend systems that connect pharmacies, health systems, and insurers. You'll solve hard distributed-systems problems including event-driven workflows, retries, idempotency, data consistency, and failure recovery. You'll own substantial features end-to-end—from problem definition through architecture, build, deployment, and production operation. You'll make pragmatic technical decisions as the company scales, avoiding overengineering for problems that don't yet exist. You'll also integrate AI-powered capabilities into a live product where reliability and correctness are as critical as speed.
You'll work directly with product and engineering leadership to shape the roadmap and architecture. The core engineering team is based in Manchester city centre with a hybrid model: 1–2 days per week in office for collaborative problem-solving and technical debate, with flexibility to work remotely the rest of the week. Relocation support is available for candidates outside Manchester.
The tech stack is modern: Node.js, TypeScript, and GCP. You don't need prior experience with these tools—the company values systems thinking and engineering depth over specific technology familiarity.
Key reasons to consider this role: your work reaches real users immediately (product is live); you'll tackle genuine distributed-systems challenges with real consequences for data integrity; you'll have real ownership over technical decisions and roadmap influence; the codebase has no legacy baggage; and you'll receive equity as part of the package.
REQUIREMENTS:
- Strong engineering judgement: ability to take ambiguous problems, identify what matters, make sensible trade-offs, and deliver without detailed step-by-step guidance.
- Backend depth: proven experience building and operating production systems; understanding of what happens beyond the happy path (distributed systems, databases, performance, observability, failure recovery).
- Ownership mindset: you think about production behavior, customer usage, failure modes, and can explain the reasoning behind technical decisions with real consequences.
- Builder's track record: meaningful projects taken from idea to reality (professional or personal); ability to articulate decisions and trade-offs.
- Curiosity about AI: experience building AI products, using AI to improve your own work, or actively exploring AI possibilities. ML research expertise not required.
- Strong fundamentals: first-principles reasoning and ability to pick up unfamiliar technologies quickly. A degree in Computer Science, Maths, Physics, or Engineering is useful, but depth of engineering ability is what matters most.
