At a Glance
- Tasks: Design and train RL agents for real-world control in live operational environments.
- Company: Fast-growing AI company focused on sustainability and innovation.
- Benefits: Competitive salary, hybrid work model, and collaboration with industry experts.
- Other info: Dynamic role with opportunities to switch between research and practical engineering.
- Why this job: Shape AI's interaction with the physical world and make a global sustainability impact.
- Qualifications: Degree in engineering/physics and strong experience in reinforcement learning.
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.
Applied Scientist employer: DMCG Global
Join a fast-growing AI company in London, where you'll be at the forefront of applying reinforcement learning to real-world challenges. With a strong focus on sustainability and innovation, we offer a collaborative work culture that encourages professional growth through hands-on experience and research opportunities. Enjoy a hybrid working model that promotes work-life balance while contributing to impactful projects that shape the future of AI in operational environments.
