At a Glance
- Tasks: Build cutting-edge reinforcement learning systems that make real-world decisions.
- Company: Innovative AI company focused on sustainability and energy efficiency.
- Benefits: Competitive salary, remote work options, and opportunities for professional growth.
- Other info: Dynamic team environment with ambitious goals and significant career advancement potential.
- Why this job: Join a pivotal moment in AI development and tackle complex, real-world challenges.
- Qualifications: Strong experience in reinforcement learning, Python, and modern ML frameworks.
The predicted salary is between 80000 - 110000 £ per year.
Build Reinforcement Learning Systems For The Real World
£80k – £110k London, Hybrid
Why is this exciting? Because this sits at the convergence of several areas that are becoming increasingly important:
- Reinforcement Learning
- Physical AI
- Digital Twins & Simulation
- Federated Learning
- Edge AI
- Critical Infrastructure
- Sustainability
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 data
- Ability to turn real-world problems into practical ML solutions
- Comfortable taking models from research into production
- Educated to degree level (or higher) in ML, Physics, Engineering, Mathematics or a related field
Nice to Have:
- Control systems, optimisation or simulation experience
- Federated Learning, Edge AI or Distributed ML
- Digital twins, thermodynamics or physical systems knowledge
- Safe RL, Offline RL, GNNs or Multi-Agent Systems
- MSc/PhD or published research in a relevant field
Ideal Profile:
Someone who enjoys solving hard, real-world problems at the intersection of AI, engineering and physical systems.
Data Science Data Science Senior Data Scientist (Remote) in City of London employer: Omnis Partners
Join a pioneering team at the forefront of AI innovation, where you'll tackle complex real-world challenges in reinforcement learning and sustainability. With a strong focus on employee growth, collaborative work culture, and the opportunity to make a tangible impact on energy efficiency, this remote role offers a unique chance to shape the future of intelligent systems. Backed by fresh funding, the company is committed to fostering an environment that encourages curiosity and creativity, making it an excellent employer for those passionate about pushing the boundaries of technology.
