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 - [email protected] or call 07464 475 407 to find out more.