Applied Scientist

🤖 Applied Scientist – Machine Learning (Reinforcement Learning) | London (Hybrid) £110kIf applying reinforcement learning to real physical systems excites you — not toy problems, not simulations, but live operational environments — this is a standout role.A fast‑growing AI company is looking for an Applied Scientist to design, train and harden RL agents end‑to‑end: from problem formulation and reward design through to federated deployment and on‑site inference. You’ll work at the intersection of ML, physics and engineering, reasoning about thermodynamics and equipment behaviour just as much as architectures and training dynamics.What you’ll be doingDesign + train RL agents for real‑world controlTurn messy telemetry into ML‑ready problemsValidate behaviour against physical principlesProductionise models — federated training, on‑site inference, monitoringSupport research + academic workWhat you bringEngineering/physics degreeStrong RL experience (deep RL, debugging, non‑trivial problems)Python + modern ML stack (PyTorch/JAX, NumPy, RL libs)Comfortable with time‑series sensor dataAbility to turn ambiguous operational challenges into tractable ML problemsHappy switching between research and practical engineeringNice to haveClassical control, MPC, HVAC, thermodynamics, power systemsSimulation, digital twins, surrogate modelsGNNs, meta‑learning, offline/safe RLFederated learning, distributed training, edge MLPublications or open‑source workSustainability‑focused optimisation experienceWhy it’s excitingYou’ll help shape how AI interacts with the physical world, working on systems with real sustainability impact at global scale — and collaborating with experts across ML, engineering and infrastructure to deploy physical‑AI responsibly and reliably.Contact me directly - james@dmcgglobal.com or call 07464 475 407 to find out more.