Why this role stands out
As a Principal Platform Engineer at Neko Health, you will have a unique opportunity to architect and build an AI developer platform from the ground up, directly impacting how software is created and deployed in a company dedicated to preventative healthcare. This hands-on technical leadership role is perfect for someone who thrives on innovation and wants to shape the future of engineering practices while working remotely. Apply to make a significant impact and drive substantial growth in your career.
Quick overview
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
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