Inherent
Member of Technical Staff (Infrastructure Engineer, Compute Infrastructure)
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Member of Technical Staff, Compute Infrastructure — Inherent (London)
At Inherent, we are on a mission to build AI that recursively self-improves to discover new knowledge. Scientific advances are the backbone of our economic, technological, and societal prosperity, but ideas are getting harder to find and breakthroughs are becoming more expensive. We are building a new frontier lab dedicated to developing AI that explores “unknown unknowns” to uncover paradigm-shifting research contributions. Science is a social endeavor, and so our mission is inextricably a human-machine teaming problem. We’re starting by reinventing the AI research factory so that our own agents accelerate their own creation.
Inherent is a well-funded, fast-growing neo-lab backed by Tier 1 VCs who believe in our ethical stance. We are a team of operators with backgrounds at frontier labs who have done foundational work in recursive self-improvement, AI Scientists, world modelling, meta-RL, and human-machine cooperation. Working in-person every day at our high-intensity London headquarters, we believe that Europe will lead the way in the coming paradigm of AI-enabled science, unlocking human potential across the globe.
About the role
We're looking for an infrastructure engineer to help make best use of cutting-edge hardware for inference and training. You'll build the operational layer of our research compute: GPU clusters, scheduling, networking, observability, and the on-call system that keeps it all running. This is the foundation that lets frontier research happen quickly, reliably, and repeatedly for both humans and AI agents. Infra here is a core part of the research process, not a support function. Inherent is a recursive company through and through, and we’re constantly closing loops from the infra level, to the scientific level, to the org level.
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What you'd do
- Run and evolve our GPU clusters: scheduling, utilisation, debugging, performance.
- Scale the Kubernetes / Linux / networking / cloud stack end-to-end.
- Establish operational excellence: incident response, postmortem culture, on-call health.
- Build agent-driven automation for cluster lifecycle, provisioning, and remediation.
- Partner directly with the research team to optimise the platform they rely on every day.
What we're looking for
- Experience operating infra at scale, preferably for LLM workloads.
- Depth in Kubernetes internals, cluster provisioning, and orchestration systems.
- Comfortable across the stack: Kubernetes, Linux, networking, containers, cloud environments.
- Strong systems thinking: you care about reliability, performance, and operational clarity.
- Good taste: you know when to build, when to buy, and when to delete.
- AI-pilled: adopting agents, keen to build a company where agents are front and centre.
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Strong candidates may also have:
- Cloud and cluster networking expertise, e.g. VPC, BGP, CNI, eBPF, service mesh.
- Experience with GPUs and CUDA.
- Infrastructure-as-code and workflow orchestration experience (Terraform and similar).
- Track record leading multi-quarter infra initiatives end-to-end.
Why this is interesting
- You'll shape the core technical foundation of a frontier AI lab from the beginning.
- The infra problems are unusually hard and creative: iteration speed for recursively self-improving agents, which themselves compound the iteration speed, is the whole game.
- Small team, high trust, no bureaucracy, and a genuinely technical culture.
- You’ll work alongside world-class colleagues with diverse backgrounds: experts in foundation model training, AI for science, and organisational design.
Culture
If you believe in our mission and culture, and are qualified and motivated, we encourage you to apply, even if you don’t meet every one of the criteria above. We know that many of the most creative and talented people have had unusual career paths and backgrounds. Building a team with a diversity of thought is mission-critical, for plurality spurs curiosity, invention, and collective experimentation.
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