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: Collaborate with experts and enjoy excellent career growth opportunities.
- 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 70000 - 90000 £ 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 are hiring an ML Engineer - Reinforcement Learning to build those agents and get them running on real data centres. You will report to the CTO / Head of AI and work across the line between research and deployment.
Key Responsibilities:
- 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.
- Control systems (classical control, MPC), 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 employer: Wave Recruitment
Join a forward-thinking company that is at the forefront of innovation in machine learning and energy efficiency. With a hybrid working model, you will enjoy the flexibility of working from home while collaborating with a talented team in our Kings Cross office just one day a week. We prioritise employee growth through continuous learning opportunities and a supportive work culture that values creativity and problem-solving, making it an excellent place for those looking to make a meaningful impact in the field of data centre operations.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer - Hybrid
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get you in the door.
✨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!
✨Tip Number 3
Prepare for the interview like it’s a big game! Research the company, understand their challenges with data centre cooling, and come armed with ideas on how you can contribute. We want to see your passion and problem-solving skills!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. We’re excited to see what you bring to the table!
We think you need these skills to ace Machine Learning Engineer - Hybrid
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 shine! Use it to explain why you're passionate about machine learning and how you can contribute to solving cooling issues in data centres. Keep it engaging and personal – we love to see your personality!
Showcase Relevant Skills:Be sure to mention your experience with Python, control systems, and any simulation tools you've used. We’re looking for someone who can hit the ground running, so highlight those skills that make you a perfect fit for our team.
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’s super easy – just follow the prompts and submit your materials!
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 examples from your experience that demonstrate your understanding of these areas and how they influence machine learning models.
✨Familiarise Yourself with Simulation Techniques
Get comfortable discussing simulation and digital twin technologies. Be prepared to explain how you’ve built or used physics-based simulators in the past, and think about how you can apply this knowledge to improve the training of reinforcement learning agents.
✨Showcase Your Collaborative Spirit
This position requires working closely with the CTO and across teams. Highlight your experience in collaborative projects, especially those involving federated learning or distributed training. Share specific instances where teamwork led to successful outcomes, as this will resonate well with the interviewers.
