At a Glance
- Tasks: Design and train reinforcement learning agents for real-world energy systems.
- Company: Small, innovative AI company focused on impactful energy solutions.
- Benefits: Competitive salary, equity options, and hybrid work model.
- Other info: Collaborate with academic partners in a dynamic, research-driven environment.
- Why this job: Join a cutting-edge team making a real difference in energy efficiency.
- Qualifications: 3-5 years of experience with deep RL in Python and teamwork skills.
The predicted salary is between 80000 - 110000 £ per year.
I'm recruiting for a small, Series A-funded AI company (~30 people) applying reinforcement learning to industrial energy systems in production, not in simulation. Their approach keeps sensitive customer data on-site, which is a genuine technical differentiator in the space. Deployments deliver significant, measurable energy savings for customers with fast payback.
What you'll be doing:
- Designing and training RL agents for live control problems
- Reward engineering, state design, and safety constraint handling
- Working across the full pipeline — from training through to on-site inference
- Contributing to a real research agenda alongside academic partners
What they need:
- 3–5 years deploying deep RL agents in Python
- Comfortable at the intersection of ML and physical systems
- Happy working in a small team where research and production sit side by side
Hybrid — 1 day/week in Central London.
Applied ML Scientist employer: Oliver Bernard
Join a dynamic and innovative AI company in London, where you'll be at the forefront of applying reinforcement learning to real-world industrial energy systems. With a strong focus on employee growth and collaboration, this small yet ambitious team offers a unique opportunity to work closely with academic partners while enjoying a hybrid work model that promotes work-life balance. Benefit from competitive compensation, equity options, and the chance to make a tangible impact on energy efficiency for customers.
