Applied Scientist
Applied Scientist – Machine Learning (Reinforcement Learning) | London (Hybrid) £110k
If 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 doing
- Design + train RL agents for real‐world control
- Turn messy telemetry into ML‐ready problems
- Validate behaviour against physical principles
- Productionise models — federated training, on‐site inference, monitoring
- Support research + academic work
What you bring
- Engineering/physics degree
- Strong RL experience (deep RL, debugging, non‐trivial problems)
- Python + modern ML stack (PyTorch/JAX, NumPy, RL libs)
- Comfortable with time‐series sensor data
- Ability to turn ambiguous operational challenges into tractable ML problems
- Happy switching between research and practical engineering
Nice to have
- Classical control, MPC, HVAC, thermodynamics, power systems
- Simulation, digital twins, surrogate models
- GNNs, meta‐learning, offline/safe RL
- Federated learning, distributed training, edge ML
- Publications or open‐source work
- Sustainability‐focused optimisation experience
Why it’s exciting
You’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.