Applied ML Scientist

The Role

We are looking for an Applied ML Scientist 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.

Duties and Responsibilities

• 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.

• Sanity-check model behaviour against physical first principles and catch

unrealistic results before they propagate downstream.

• Work alongside software engineers to productionise models on the OctaiPipe

platform, including federated training pipelines, on-site inference, and

monitoring of deployed policies.

Restricted

• Support the team's research agenda, including collaborations with academic

partners and (where appropriate) external technical write-ups.