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, secure and sustainable.

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

OctaiPipe 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 the ACE team and work on reinforcement learning applied to real, physical infrastructure.

You will help develop, train, and harden RL agents that operate in live data centre environments, working across the full arc from problem formulation and model training through to federated deployment and inference on customer sites.

The work sits at the intersection of machine learning and engineering: you will spend as much time reasoning about thermodynamics, equipment behaviour, and operational constraints as you do about model architectures and training dynamics.

• Design, train, and evaluate reinforcement learning agents for control problems in data centre environments.
• Translate messy, real-world telemetry into well-posed ML problems, including state and action design, reward engineering, and constraint handling for safety-critical operation.