Applied Scientist, Machine Learning

OctaiPipe is a young, ambitious company with the vision to be the global driving force for the next paradigm of foundational, physical AI that ensures our connected world, and its critical infrastructure, is safe, sec ure an d sustainable.

We are growing fast, having closed a recent funding round and looking to accelerate rapidly.

Oct aiPipe is offering the right candidate an exciting role on this adventure !

OctaiPipe is on a mission to revolutionise the optimisation of energy in data centres through decentralised artificial intelligence (AI).

To do this, OctaiPipe is harnessing an elegant but revolutionary idea.

Rather than move the data from the source, move the algorithms to the data to learn at the data source.

This learning can be achieved with the intelligence of many devices through novel federated AI technology.

OctaiPipe is developing the AI for Cooling Efficiency (ACE) application to be deployed using its own in-house distributed AI platform.

We are looking for an Applied Scientist, Machine Learning to join our Physical Modelling team.

You'll develop data-driven surrogate and digital twin models for real industrial assets, exploring how they're built, evaluated and continuously improved using data from live deployments.

You'll frame research questions, design and run experiments, and translate insights into robust models that our engineering teams can take into production.

A key part of the role is understanding how these models behave in high-stakes environments and helping determine when they can be trusted to support real-world decision-making.

• Develop, evaluate and advance the state of the art in data-driven surrogate / digital-twin methodologies .
• Own a research agenda end to end - from problem framing through experimentation to results that production systems consume - with a high degree of independence.