Senior Data Platform Engineer

About the role

At CoMind, we are developing a non-invasive neuromonitoring technology that will result in a new era of clinical brain monitoring. In joining us, you will be helping to create cutting-edge technologies that will improve how we diagnose and treat brain disorders, ultimately improving and saving the lives of patients across the world.

The Role: CoMind One measures the brain from outside the skull. Fifteen scientists (optical physicists, signal processing specialists, physiologists) turn raw optical interference signals into continuous measurements of cerebral physiology a clinician can act on. What slows them down isn't the science; it's the engineering around it. The last head-to-head comparison of two novel methods took six weeks, and most of that had nothing to do with the methods. You'd design the architecture that fixes this: how data, compute, pipelines and evaluation fit together, and the standards that hold it in place. None of it exists yet in any deliberate form, so the shape is an open question. When it works, a scientist describes the comparison they want and gets back metrics, plots and a reproducible record from one command, or one prompt.

At CoMind, all team members work at least 4 days per week from our new Kings Cross offices, plus a flexible work-from-home day.

Responsibilities: Problems, not specifications. How they get solved is yours to decide.

- Reprocessing. Change one stage of the pipeline and you currently pay for a full re-run. Re-running a six-month-old analysis with new parameters should be routine and cheap.

- Evaluation. Comparison and simulation harnesses are rebuilt bespoke every time. They should be something a scientist calls, not something a scientist commissions, including scientists who don't write code, and agents running analysis strands on their behalf.

- Compute and data access. Reproducible environments, sensibly-sized cloud resource, and datasets you can find and trust without asking whoever made them.

- Engineering standards. Testing, CI and repository structure across a codebase written largely by scientists. Knowing where to enforce and where to reduce friction instead matters more than the tooling does.

- The research-to-software boundary. We already have a real interface contract with our software team, which is more than most research groups can say. It should become a gate a method passes, not an event that consumes weeks.

- Regulatory evidence as a by-product. IEC 62304 traceability artefacts generated from CI rather than written alongside it.

- Growing the capability. Mentoring junior developers in the team, and shaping this function as it grows.

AI is fundamental to our culture. It's not just a tool, but a core part of how we work, collaborate, and innovate. We expect all team members to embrace AI in their daily work and continuously find new ways to use it effectively.

Skills & Experience:

- Substantial software or platform engineering experience, a good deal of it building tooling for scientists, researchers or quants rather than for end customers