At a Glance
- Tasks: Build and deploy reinforcement learning agents to optimise data centre cooling systems.
- Company: Innovative tech firm focused on energy efficiency and AI solutions.
- Benefits: Hybrid work model, competitive salary, and visa sponsorship available.
- Other info: Collaborative environment with opportunities for growth and innovation.
- Why this job: Make a real impact on energy savings while working with cutting-edge AI technology.
- Qualifications: Experience in deep reinforcement learning and a background in physical systems.
The predicted salary is between 60000 - 80000 £ per year.
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. 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.
- Build and improve the physics-based simulators, surrogate models, and digital twins the agents train against.
- Federated and distributed training across sites.
- Edge deployment, monitoring, and retraining of agents already running in production.
Requirements:
- 3-5 years training and deploying deep RL agents in Python.
- 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.
- 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.
Hybrid working, one day a week in the Kings Cross office. Visa sponsorship available on a case-by-case basis.
Machine Learning Engineer - Hybrid in London employer: Wave Recruitment
Join a forward-thinking company that prioritises innovation and sustainability in the data centre industry. As a Machine Learning Engineer, you'll enjoy a hybrid work model that promotes work-life balance while collaborating with top-tier professionals in a dynamic environment. With opportunities for professional growth and a commitment to cutting-edge technology, this role offers a meaningful chance to make a significant impact on energy efficiency and operational excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer - Hybrid in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We can’t stress enough how important it is to make connections; you never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to reinforcement learning and simulations. We love seeing real-world applications of your work, so make sure to highlight any relevant experience.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. We recommend practising common ML scenarios and being ready to discuss your thought process. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always on the lookout for passionate candidates who want to make a difference in the world of data centres.
We think you need these skills to ace Machine Learning Engineer - Hybrid 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 achievements!
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 machine learning and how you can contribute to solving data centre cooling issues. Keep it engaging and personal!
Showcase Relevant Experience:If you've got experience with federated learning, digital twins, or control systems, make sure to highlight that in your application. We love seeing candidates who can bring practical knowledge to the table!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Wave Recruitment
✨Know Your Reinforcement Learning
Make sure you brush up on your knowledge of reinforcement learning concepts, especially as they relate to real-world applications like cooling systems. Be ready to discuss how you've applied these principles in past projects or how you would approach building agents for data centres.
✨Understand the Physical Systems
Since the role involves working with physical systems, it’s crucial to have a solid grasp of engineering principles related to HVAC, thermodynamics, and control systems. Prepare to explain how your background in these areas can contribute to solving cooling issues effectively.
✨Familiarise Yourself with Simulation Techniques
Get comfortable discussing simulation and digital twin technologies. Be prepared to share any experience you have with building or using physics-based simulators, as this will be key in training the agents you'll be working with.
✨Showcase Your Teamwork Skills
This position requires collaboration across various teams, so highlight your experience working in hybrid environments and your ability to communicate complex ideas clearly. Think of examples where you’ve successfully worked with others to achieve a common goal.
