Machine Learning Engineer - Hybrid

ML Engineer - Reinforcement Learning London (hybrid, 1 day/week in Kings Cross)- Solve Data Centres Cooling issues Cooling is one of the largest items on a data centre's energy bill, and most sites run it conservatively because getting it wrong puts the hardware at risk. Our client trains reinforcement learning agents to control cooling systems on live sites, cutting cooling energy without breaching the temperature and humidity limits operators are contractually bound to. They're hiring an ML Engineer - Reinforcement Learning to build those agents and get them running on real data centres. You'll report to the CTO / Head of AI and work across the line between research and deployment. They train against a digital twin of each site, then move to production once they're safe. Reward and constraint design is shaped by ASHRAE standards and customer SLAs - air temperature, humidity, and rate-of-change limits on cooling air and chilled water setpoints Training is federated across multiple sites. Agents share learned control strategies without any site's operational data leaving the building, which delivers significantly more savings than a single-site approach Simulation and Digital Twins Build and improve the physics-based simulators, surrogate models, and digital twins the agents train against Production and Deployment Federated and distributed training across sites Edge deployment, monitoring, and retraining of agents already running in production 3-5 years training and deploying deep RL agents in Python ~ A background in physical systems - engineering (mechanical, electrical, structural, biomedical), physics, robotics, autonomous driving, or control systems - and the instinct to reason about what's physically possible, not only what's mathematically possible ~ Control systems (classical control, MPC), or HVAC, thermodynamics, power systems, or data centre operations Federated learning, distributed training, or edge ML deployment Simulation experience - building or using physics-based simulators, digital twins, surrogate models, or large physics models Hybrid working, one day a week in the Kings Cross office ~ Visa sponsorship available on a case-by-case basis