Computer Science and Web Development

Cardiorespiratory disease is the leading cause of death worldwide. For most of the people living with it, monitoring remains infrequent, inconvenient, and tethered to a hospital. Our platform, lung

Dx, changes those conditions. Using a smartphone alone, with no additional hardware and no specialist environment, the technology captures optical, biomechanical, and physiological signals that until recently required clinical instrumentation.

We are past the point of proving that it works. The validation data is published, and we hold Class IIa authorisation from the MHRA as a manufacturer of Software as a Medical Device. What we are building now is the infrastructure to carry it to hundreds of thousands of people across several countries and clinical programmes.

That is what makes the timing of this role unusual. You would arrive at the point where a proven technology becomes an operating system for care at scale, and the decisions taken this year will still be visible in the product long after you have left.

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Rx exists because people living with these conditions deserve better information about their own bodies, more often, and without having to travel to a hospital to obtain it. That conviction decides what we build and it decides what we refuse to build. We hire on the same basis. Technical ability matters and we will test for it, but we care rather more about whether you stay curious once a problem stops being tidy.

We are looking for a technically capable graduate or placement student who is comfortable working across data science and web development: from the pipeline that processes raw physiological signals through to the interface where clinicians and participants meet the results.

This is a generalist role by design. You will move between real-world datasets, clinical data workflows, internal tooling, and applications used by investigators and participants. The work shifts according to where the team needs momentum, which is a feature of the role rather than a limitation of it. If a changing problem energises you, you will do well here.

You will not be maintaining a legacy system or running regression scripts in a corner. You will build software that underpins live clinical investigations, contribute to deployments in other countries, and work directly alongside data scientists, engineers, and clinicians. The team is small enough that your work is visible and its consequences are immediate.

The role is advertised as a 12-month placement or graduate internship at £25,000 per annum. For a graduate who is clearly the right fit, we would equally consider a permanent full-time appointment paying up to £32,500 depending on experience. Tell us in your covering note which you are looking for, and say so if you are open to either.

Preparing, exploring, and analysing physiological and clinical datasets from live investigations

Improving the systems through which participant data is captured, processed, and stored

Building internal tools, dashboards, and web applications used by clinicians and by the commercial team

Prototyping quickly across software and data workflows, so that ideas get tested rather than debated

Supporting the development of statistical, signal processing, and machine learning methods for healthcare

Working directly with data scientists, engineers, and clinicians on practical problems

Explaining what you have found to people who do not share your discipline

Holding several concurrent projects with different requirements and timescales

You will be studying towards, or will already hold, a degree in Computer Science, Mathematics, Physics, Engineering, or a similarly analytical discipline. Strong Python is the baseline. Beyond that we would far rather see real depth in two or three areas than a passing acquaintance with everything on the list. If your foundations are solid and you want to go further, we will back you to develop the rest.

Strong programming ability in Python

Analytical and problem-solving ability applied to real data rather than coursework datasets

Working knowledge of databases and data-driven applications

Fluency with AI development tools as part of how you already work, not as a novelty

The ability to explain your reasoning clearly to people outside your discipline

Signal processing or time-series analysis

Cloud platforms: GCP, AWS, or Firebase

SQL or No

SQL databases

Machine learning model development or evaluation

Node.js or C++

Any exposure to…