Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and implement innovative machine learning algorithms for quantum computing applications.
- Company: Exciting early-stage start-up at the forefront of quantum technology.
- Benefits: Hybrid work flexibility, exposure to pioneering tech, and opportunities for impactful contributions.
- Why this job: Join a team pushing the boundaries of ML and quantum tech to solve real-world challenges.
- Qualifications: Experience in ML algorithms, strong Python skills, and advanced degree in a technical field.
- Other info: Collaborative environment with opportunities for scientific innovation and career growth.
The predicted salary is between 60000 - 80000 £ per year.
We’re looking for a Machine Learning Research Engineer to join an early-stage quantum computing start-up developing full-stack photonic quantum systems. This role sits at the intersection of advanced ML research and applied engineering, focusing on generative algorithms and hybrid quantum-classical models. You’ll work closely with clients and partners to show how ML and quantum technologies can solve real-world challenges.
Responsibilities:
- Design, implement, and evaluate new machine learning algorithms, particularly generative models (GANs, flow models, diffusion models) and hybrid quantum-classical neural networks.
- Partner with clients to translate practical problems onto the company’s quantum hardware and deliver solutions that demonstrate value.
- Expand and improve the company’s software platform with new algorithms, example applications, and research-driven features.
- Support scientific innovation by publishing findings and helping safeguard intellectual property.
Required Skills & Experience:
- Proven experience building and testing machine learning algorithms.
- Hands-on experience with GANs, diffusion models, or flow-based generative models.
- Strong programming skills in Python, PyTorch, and version control (Git).
- Advanced degree (Master’s or PhD) in Computer Science, Physics, or a related technical field.
Preferred Skills:
- Experience working with specialized computing hardware (HPC, NPUs, ASICs, or quantum processors).
- Multi-GPU training experience.
- Publication record in ML or AI research.
- Comfortable presenting and collaborating with clients or stakeholders.
- Familiarity with quantum computing principles.
Why Join:
This role offers the chance to work on cutting-edge ML research applied to quantum hardware, delivering impactful solutions that move from concept to real-world implementation. The position provides hybrid flexibility in London and direct exposure to pioneering quantum technology.
GPU Machine Learning Engineer in England employer: Metric
Contact Detail:
Metric Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land GPU Machine Learning Engineer in England
✨Tip Number 1
Network like a pro! Reach out to people in the quantum computing and machine learning fields on LinkedIn. Join relevant groups and participate in discussions to get your name out there and show off your passion.
✨Tip Number 2
Showcase your skills! Create a portfolio of your projects, especially those involving GANs or hybrid models. This will give potential employers a taste of what you can do and how you can contribute to their team.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with Python, PyTorch, and any hands-on work with quantum hardware. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals who are eager to dive into the world of quantum technologies and machine learning. Your dream job could be just a click away!
We think you need these skills to ace GPU Machine Learning Engineer in England
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning algorithms, especially generative models like GANs and diffusion models. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the intersection of ML and quantum computing. Share specific examples of how you've tackled real-world problems using your skills, and let us know why you want to join our team.
Show Off Your Projects: If you’ve got any personal or academic projects that demonstrate your programming skills in Python or your experience with PyTorch, include them! We love seeing practical applications of your knowledge, especially if they relate to quantum technologies.
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 don’t miss out on any important updates. Plus, it shows us you’re keen to be part of our journey!
How to prepare for a job interview at Metric
✨Know Your Algorithms
Make sure you brush up on generative models like GANs, diffusion models, and flow-based models. Be ready to discuss your experience with these algorithms in detail, as well as any challenges you've faced while implementing them.
✨Showcase Your Programming Skills
Since strong programming skills in Python and PyTorch are crucial for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice coding exercises and be familiar with version control using Git.
✨Understand Quantum Computing Basics
Familiarise yourself with quantum computing principles, especially how they relate to machine learning. Being able to discuss how ML can be applied to quantum hardware will show your enthusiasm and understanding of the field.
✨Prepare for Client Interaction
As this role involves working closely with clients, think about examples from your past experiences where you've successfully collaborated or presented solutions. Highlight your communication skills and ability to translate complex technical concepts into practical applications.