Here Is What That Looks Like In Practice

• You work side by side with clients to scope, design, build, and test new data products on top of the Promptly platform. You own the full cycle, from first conversation to something live.
• You co-design with clinicians and patients at the point of capture — sitting in clinic, understanding why a form goes unanswered, and building the version that gets used. Completion and cadence are engineering outcomes you own, not someone else's adoption problem.
• You model real clinical data: mapping partner source systems into OMOP, designing PROMs and registry structures, and building phenotype and concept definitions that hold up when the next client reuses them.
• You develop AI-powered solutions with clients, using the platform's capabilities and your own tooling to solve problems they didn't think were solvable yet.
• You build in the sandbox before the real data is ready — using synthetic and multimodal cohorts to prototype, prove feasibility, and give a client something working to react to in weeks rather than quarters.
• You create custom workflows, automation logic, and integrations that fit each client's operational reality. You don't configure templates; you build things that are genuinely new.
• You work across modalities — structured records, patient-reported data, device and wearable telemetry, imaging, and genomics — and across governance regimes where raw data does not move.
• You treat every client engagement as a product sprint. You identify the real problem, build toward a solution, test it with real users, and iterate until it works.
• You bring what you learn back into Promptly. Every pattern you see, every gap you find, every solution you build: it feeds product and engineering and makes the platform better for everyone.

How We Build With AI

At Promptly, AI-assisted development is part of how we work. We expect engineers to use coding agents, copilots, code search, AI-assisted debugging, and automated review to move faster through real engineering work.

We expect engineers to use AI as part of a modern development workflow, with the same standards of craft, quality, and accountability we apply to any production system. Strong engineers know where AI accelerates the work, where it introduces risk, and where human judgment needs to lead.

You own what you ship. You should be able to explain the design, inspect the diff, validate the behaviour, check the security and privacy implications, debug failures, and operate the system in production. If you cannot understand it, test it, secure it, and maintain it, you should not merge it — and if you cannot explain to a clinician why a system produced a given output, you should not put it in front of one.

What We're Looking For

This role is grounded in blended healthcare domain expertise, the ability to build trust inside healthcare organisations, and a track record of delivering real technical work in that setting. Those three things together are the job. Strong, demonstrable programming and data capability is essential — but it is the second requirement, not the first.

Required

• Demonstrable blended healthcare domain knowledge. You understand how clinical care, data, operations, and governance actually interact — not from the outside, but from having worked in or alongside healthcare organisations. You can tell the difference between a problem that looks technical and one that is really about pathway, incentive, or trust.
• Proven technical delivery in a healthcare setting. You have built and shipped things that clinical or operational users genuinely used. Not proofs of concept that stalled, not designs handed to someone else to implement — working systems, in live environments, with your name on the outcome.
• Trust-building with clinical and organisational stakeholders. You have earned the confidence of clinicians, information governance leads, and organisational leadership, and you understand that access in healthcare is earned rather than bought. You know how to be the person a clinical team calls.
• Experience working closely with, or within, local or national healthcare organisations. NHS trusts or health boards, national bodies and programmes, hospital networks, disease registries, or their equivalents in other European health systems.
• Direct experience with clinicians, patient groups, or healthcare executives. You have run design or engagement work with at least one of these groups and changed what you built as a result.
• C-suite engagement and value realisation. You can define what value an engagement is meant to deliver, articulate it in terms a healthcare board or executive team recognises, and evidence whether it actually landed. You are comfortable being in the room when that is assessed.
• Information governance and ethics experience. DPIAs, ethics and research approvals, data sharing agreements, IG committees, pseudonymisation, and working in environments where raw data cannot leave the owner. You treat governance as part of the build, not an obstacle to it.
• Strong, demonstrable programming and data capability. You write and debug your own code to a standard you would defend in review. Python and SQL are working tools for you, and you are comfortable picking up a modern application stack where an engagement needs one. We care that you can build; we do not require that "software engineer" has ever been your title.
• Hands-on experience with real clinical or health data. You have worked with the mess of hospital records, registries, or patient-reported data, not just clean extracts — and ideally with a clinical common data model such as OMOP.
• Practical understanding of AI/LLM-powered products. You understand how model behaviour, evaluation, reliability, and user experience affect systems that people rely on, and where human judgment has to lead.
• High agency in ambiguous, complex environments. You can make progress even when requirements, ownership, or process are not perfectly defined, and you help create clarity as you go.
• The ability to switch registers. A technical conversation with engineers and a strategic conversation with clinical, operational, or executive stakeholders — sometimes in the same meeting.
• Strong ownership and urgency. You treat client outcomes as your problem, follow issues through to resolution, and move quickly when something is not working.
• Good judgment under pressure. You simplify complexity, spot risks early, and make sound decisions without creating unnecessary noise.
• A collaborative, low-ego working style. You can work across product, engineering, customer success,