About the role
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