Principal Platform Engineer

Neko Health; Greater London; On-site; GBP 140,000 - 200,000; Full time

Job summary

Neko Health AB is seeking a director-level Principal Engineer to build the shared AI engineering platform that enables rapid, compliant AI-driven software development for regulated medical devices. You will define architecture, set governance, and lead end-to-end AI-enabled engineering across teams, including devices and test rigs.

This senior individual contributor role requires shaping the fastest path to shipping with AI, ensuring auditability, traceability, and compliance while collaborating

Qualifications

• You have designed and built platforms or core infrastructure that other engineers relied on every day, and you wrote a meaningful part of the code yourself.
• Deep backend and distributed systems experience in production, with the judgement to make architecture calls in a large, existing codebase.
• Your own engineering is AI‑first: you use agents for work you used to do by hand, and can explain where they help, where they fail, and what changed in speed and quality.
• You take a clear position on hard technical questions with incomplete information, defend it with reasons, and change it when the evidence changes.
• Engineers outside your own team changed how they work because of something you built and the technical case you made for it.
• Able to work from our London, New York or Stockholm office.

Responsibilities

• 0→1 set the target architecture for the AI engineering platform, including the build or adopt decisions on agents, orchestration and developer tooling
• Ship a golden path for AI-assisted engineering, with shared context, tools and guardrails, that teams choose because it is the fastest way to ship
• Put agents to work on real engineering jobs end to end, such as feature implementation, code migrations, automated review and on‑call triage
• Run agents in sandboxed environments with audit trails and clear separation from member health data
• Shorten the path from merged code to production for regulated software: safe releases, fast rollback, and compliance evidence produced by the pipeline
• Measure what changes with numbers engineers trust, such as the share of agent pull requests that get merged and how long a change takes to reach production, and share them openly
• Take on the hardest cross‑team technical problems, inside and beyond the platform, where no one has the answer yet

Job description

Mission

Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it’s ever at risk. Our mission: make data-driven, preventative care accessible to more people, before symptoms appear.

In a single, non-invasive visit under an hour, proprietary technology and direct clinical care combine to deliver personalised, actionable insights. It’s a team that thinks in 10x, not 10%. Every role here plays a part in building a world where prevention is the norm, and where your work genuinely helps people live longer, healthier lives.

Role Purpose

We want our engineers to ship regulated medical software as fast as AI now lets them write it. Our software includes medical device software, holds members’ health data and keeps our clinics running, so every change has to be tested, traceable and ready for an auditor, and that is what makes this problem hard and worth solving.

You’ll build the shared AI engineering platform that makes it possible, setting the architecture and writing much of it yourself: a golden path that makes the best way to build with AI the default for every team, agents that do real engineering work end to end, and a pipeline that proves compliance on every change instead of documents written after the fact. This is a director-level individual contributor role, one of the first Principal Engineer roles in our software organisation, working directly with our Chief Software Officer. You make the key calls on which agents and tools we build or adopt, and you take on the hardest problems across teams, some of which reach clinic devices and the test rigs that recreate a scan room.

What You’ll Deliver in the First 6–12 Months

• 0→1 set the target architecture for the AI engineering platform, including the build or adopt decisions on agents, orchestration and developer tooling

• Ship a golden path for AI-assisted engineering, with shared context, tools and guardrails, that teams choose because it is the fastest way to ship

• Put agents to work on real engineering jobs end to end, such as feature implementation, code migrations, automated review and on‑call triage

• Run agents in sandboxed environments with audit trails and clear separation from member health data

• Shorten the path from merged code to production for regulated software: safe releases, fast rollback, and compliance evidence produced by the pipeline

• Measure what changes with numbers engineers trust, such as the share of agent pull requests that get merged and how long a change takes to reach production, and share them openly

• Take on the hardest cross‑team technical problems, inside and beyond the platform, where no one has the answer yet

Minimum Qualifications

• You have designed and built platforms or core infrastructure that other engineers relied on every day, and you wrote a meaningful part of the code yourself

• Deep backend and distributed systems experience in production, with the judgement to make architecture calls in a large, existing codebase

• Your own engineering is AI‑first: you use agents for work you used to do by hand, and can explain where they help, where they fail, and what changed in speed and quality

• You take a clear position on hard technical questions with incomplete information, defend it with reasons, and change it when the evidence changes

• Engineers outside your own team changed how they work because of something you built and the technical case you made for it

• Able to work from our London, New York or Stockholm office

Preferred Qualifications

• Experience with durable workflow orchestration, for example Temporal or Azure Durable Functions, and a clear view on when it is worth it, since long-running agent work has to survive failure

• Work where software meets hardware or physical operations, such as connected devices, edge computing or hardware-in-the-loop testing