Senior Data Scientist

Most AI companies are building tools that generate content. This team is building AI that makes decisions in the real world.

Backed by fresh funding and entering a major growth phase, they’re developing a new generation of AI systems capable of learning, adapting and optimising complex physical environments. Their first challenge? Reducing the energy consumption of data centres through reinforcement learning and distributed AI.

The problems are messy, ambiguous and genuinely difficult.

You’ll be working with real telemetry, real constraints and real-world systems where model performance has a direct impact on energy efficiency, sustainability and operational outcomes. This isn’t about tweaking prompts or wrapping foundation models. It’s about building intelligent systems that can learn how the physical world behaves and make better decisions because of it.

They’re looking for someone who combines strong machine learning fundamentals with the curiosity to understand how complex systems actually work. Someone who enjoys moving between research, experimentation and production, and gets excited by solving problems that don’t already have a playbook.

You’ll have the opportunity to work across reinforcement learning, simulation, federated learning and next-generation AI systems, helping shape technology that extends far beyond a single use case.

For the right person, this is a chance to join at a pivotal moment. The team is growing, the roadmap is ambitious, and the technical challenges are the kind that attract people who want to push the boundaries of what AI can actually do.

Experience required:Strong hands-on experience with Reinforcement Learning (RL)Python and modern ML frameworks (PyTorch, JAX, TensorFlow)Experience working with time-series or sensor dataAbility to turn real-world problems into practical ML solutionsComfortable taking models from research into productionEducated to degree level (or higher) in ML, Physics, Engineering, Mathematics or a related field Nice to HaveControl systems, optimisation or simulation experienceFederated Learning, Edge AI or Distributed MLDigital twins, thermodynamics or physical systems knowledgeSafe RL, Offline RL, GNNs or Multi-Agent SystemsMSc/PhD or published research in a relevant field Ideal ProfileSomeone who enjoys solving hard, real-world problems at the intersection of AI, engineering and physical systems.