At a Glance
- Tasks: Train and deploy reinforcement learning agents for real-time cooling control in data centres.
- Company: Innovative tech firm tackling energy efficiency with cutting-edge AI solutions.
- Benefits: Competitive salary, equity options, hybrid work, and direct access to leadership.
- Other info: Opportunity for career growth and involvement in impactful projects.
- Why this job: Solve real-world problems while working on groundbreaking technology in a dynamic environment.
- Qualifications: 3-5 years in deep RL, Python, and a background in physical systems.
The predicted salary is between 110000 - 150000 £ per year.
London (hybrid, 1 day/week in Kings Cross) - Solve Data Centres Cooling issues. Cooling is one of the largest items on a data centre's energy bill, and most sites run it conservatively because getting it wrong puts the hardware at risk. Our client trains reinforcement learning agents to control cooling systems on live sites, cutting cooling energy without breaching the temperature and humidity limits operators are contractually bound to.
They're hiring an ML Engineer - Reinforcement Learning to build those agents and get them running on real data centres. You'll report to the CTO / Head of AI and work across the line between research and deployment.
The System
- The agents don't learn on the live plant. They train against a digital twin of each site, then move to production once they're safe.
- Reward and constraint design is shaped by ASHRAE standards and customer SLAs - air temperature, humidity, and rate-of-change limits on cooling air and chilled water setpoints.
- Training is federated across multiple sites. Agents share learned control strategies without any site's operational data leaving the building, which delivers significantly more savings than a single-site approach.
- Models are deployed on-prem at the edge, then monitored and retrained in place.
What You'll Own
- Reinforcement Learning
- Train and deploy deep RL agents for live cooling control.
- Design reward functions and constraints that hold up against physical limits and SLAs, not just in a notebook.
- Move between research-style exploration and the engineering work to make something stable on a real site.
- Simulation and Digital Twins
- Build and improve the physics-based simulators, surrogate models, and digital twins the agents train against.
- Close the gap between what works in simulation and what holds on real hardware.
- Production and Deployment
- Federated and distributed training across sites.
- Edge deployment, monitoring, and retraining of agents already running in production.
What We're Looking For
Essential
- 3-5 years training and deploying deep RL agents in Python.
- PyTorch or JAX, and RL libraries such as Gymnasium.
- A background in physical systems - engineering (mechanical, electrical, structural, biomedical), physics, robotics, autonomous driving, or control systems - and the instinct to reason about what's physically possible, not only what's mathematically possible.
- Comfortable iterating between research exploration and the engineering needed to run on a live site.
- A degree in engineering, CS, or physics.
Useful
- Control systems (classical control, MPC), or HVAC, thermodynamics, power systems, or data centre operations.
- Federated learning, distributed training, or edge ML deployment.
- Simulation experience - building or using physics-based simulators, digital twins, surrogate models, or large physics models.
- Published research or open-source contributions.
Who You Are
You want both halves of this job. You'll run experiments and read papers, but you also want your work controlling real equipment, with the constraints that come with that. RL experience limited to advertising or multi-armed bandits won't carry over here - the physical world doesn't behave like a recommendation system. A pure maths or CS background with no feel for physical systems will struggle, and so will anyone after a pure research seat or a pure production one. This sits in the middle.
What's On Offer
- £110K-£150K, plus competitive equity.
- A genuine technical problem: RL on physical systems, under real constraints, deployed on live infrastructure.
- Direct access to the CTO and founding team.
- Hybrid working, one day a week in the Kings Cross office.
- Visa sponsorship available on a case-by-case basis.
Get in touch for a confidential conversation. Imogen@waverecruitment.co.uk
Machine Learning Engineer- Reinforcement Learning in London employer: Wave Recruitment
Join a forward-thinking company that is at the forefront of utilising reinforcement learning to optimise data centre cooling systems, significantly reducing energy costs while ensuring operational safety. With a hybrid working model and direct access to the CTO and founding team, you will thrive in a collaborative environment that values innovation and offers substantial growth opportunities. The unique challenge of applying advanced machine learning techniques to real-world physical systems makes this an exciting and rewarding role for those passionate about impactful technology.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer- Reinforcement Learning in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to reinforcement learning and physical systems. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on both technical and practical aspects of ML. Be ready to discuss how you’d tackle real-world problems, like cooling systems in data centres, and demonstrate your understanding of constraints and rewards.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Machine Learning Engineer- Reinforcement Learning in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with reinforcement learning and any relevant projects you've worked on. We want to see how your skills align with the role, so don’t be shy about showcasing your Python and PyTorch expertise!
Craft a Compelling Cover Letter:Your cover letter is your chance to tell us why you're the perfect fit for this role. Share your passion for solving real-world problems with ML and how your background in physical systems makes you a strong candidate. Keep it engaging and personal!
Showcase Relevant Projects:If you've worked on any projects involving digital twins or federated learning, make sure to mention them! We love seeing practical applications of your skills, so include links to your GitHub or any published research if you have them.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen to join the StudySmarter team!
How to prepare for a job interview at Wave Recruitment
✨Know Your Reinforcement Learning
Make sure you brush up on your reinforcement learning concepts, especially how they apply to physical systems. Be ready to discuss your experience with training and deploying deep RL agents, and how you've tackled real-world constraints in past projects.
✨Showcase Your Technical Skills
Prepare to demonstrate your proficiency in Python, PyTorch or JAX, and relevant RL libraries like Gymnasium. Bring examples of your work that highlight your ability to iterate between research and engineering, especially in the context of live deployments.
✨Understand the Physical Systems
Familiarise yourself with the principles of HVAC, thermodynamics, and data centre operations. Be prepared to explain how your background in engineering or physics informs your approach to designing reward functions and constraints for RL agents.
✨Engage with the Team
Since you'll be reporting directly to the CTO, it's important to show your enthusiasm for collaboration. Prepare thoughtful questions about the company's approach to federated learning and edge deployment, and express your eagerness to contribute to their innovative solutions.
