At a Glance
- Tasks: Develop and deploy reinforcement learning agents for optimising data centre cooling systems.
- Company: Innovative tech firm focused on sustainable energy solutions.
- Benefits: Hybrid working, 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 efficiency while working with cutting-edge technology.
- Qualifications: 3-5 years in deep RL, Python, 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 (Ethics) in London employer: Wave Recruitment
As a Machine Learning Engineer (Ethics) at our innovative company, you'll be part of a forward-thinking team dedicated to solving critical energy efficiency challenges in data centres. With a hybrid working model that allows for flexibility and collaboration in our Kings Cross office, we foster a culture of continuous learning and professional growth, ensuring you have the resources and support to excel in your role. Join us to make a meaningful impact on sustainability while working with cutting-edge technology in a dynamic environment.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer (Ethics) 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 digital twins. We love seeing practical applications of your work, so make sure to highlight any real-world impact you've made.
✨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. Remember, it’s not just about getting the right answer but how you approach the problem!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate candidates who are eager to make a difference in the field of machine learning.
We think you need these skills to ace Machine Learning Engineer (Ethics) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with reinforcement learning, digital twins, and any relevant projects that showcase your skills in Python and control systems.
Craft a Compelling Cover Letter:Your cover letter should tell us why you're passionate about this role and how your background aligns with our mission. Mention specific experiences that relate to cooling systems or federated learning to grab our attention!
Showcase Your Projects:If you've worked on any projects involving simulation, digital twins, or edge ML deployment, make sure to include them. We love seeing practical applications of your skills, so don’t hold back!
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Wave Recruitment
✨Know Your Stuff
Make sure you brush up on reinforcement learning concepts and how they apply to cooling systems. Be ready to discuss your experience with deep RL agents, especially in Python, and how you've tackled similar challenges in the past.
✨Understand the Business
Get familiar with the company's focus on energy efficiency in data centres. Knowing about ASHRAE standards and customer SLAs will show that you understand the constraints and rewards involved in the role, which is crucial for a Machine Learning Engineer.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've built or improved physics-based simulators or digital twins. Highlight your experience with federated learning and edge ML deployment, as these are key aspects of the job.
✨Ask Smart Questions
Come prepared with insightful questions about their current projects and future goals. This not only shows your interest but also gives you a chance to demonstrate your understanding of the technical challenges they face in deploying ML solutions.
