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 endeavour, 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 build the training and inference systems that frontier research depends on. You'll build and maintain the systems our researchers — and our AI Scientists themselves — depend on to train and serve models. The focus is on speed, reliability, and ease-of-use, with a particular emphasis on large-scale distributed RL and deployment in scientific domains. 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.

What you'd do

• Contribute to our training + inference infrastructure end-to-end.

• Build systems to run large-scale experiments: distributed job orchestration, data pipelines, evals.

• Implement and harden RL and post-training libraries for our models.

• Close recursive loops so AI agents can drive their own training and infrastructure.

• Work closely with the core research team on the systems they live inside.

What we're looking for

• High-performance, large-scale distributed systems experience, preferably for LLM workloads.

• Proficiency in Python and PyTorch or JAX.

• Hands-on experience with large-scale LLM training or inference technologies, e.g. SGLang, vLLM, verl, Megatron, OpenRLHF.

• 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.

Strong candidates may also have

• Experience with a systems programming language like Rust or C++.

• Experience with writing and profiling CUDA kernels.

• A track record of building reliable research tools.

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.