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For Companies At a Glance
- Tasks: Join a team to build and optimise deep learning models for medical imaging.
- Company: Be part of an innovative clinical tech company transforming healthcare with AI.
- Benefits: Enjoy remote work flexibility, autonomy, and a culture that values collaboration.
- Why this job: Shape impactful AI solutions that improve patient outcomes in a supportive environment.
- Qualifications: Strong experience in deep learning and medical imaging; MSc or PhD preferred.
- Other info: Opportunity for occasional travel to the UK and collaboration with top AI experts.
The predicted salary is between 42000 - 76000 £ per year.
Location: Remote (Europe preferred), with occasional travel to the UK if needed
Salary: £60,000–£95,000 GBP equivalent, depending on experience
About the Opportunity:
We’re supporting an ambitious clinical technology company who are building a next-generation AI platform for 3D imaging and diagnostic surgery planning. This is an opportunity to join the first wave of technical hires, working directly alongside a former AI leader from a global healthcare innovator. You'll help shape a platform focused on transforming clinical outcomes through AI-based imaging interpretation. You’ll be joining at the ground floor — helping to design, build, and deploy deep learning models (not just apply pre-trained ones) for medical-grade use cases.
The Role:
- You’ll work closely with AI researchers, software engineers, and clinicians to:
- Build, train, and optimise deep learning models (3D CNNs, segmentation models, landmark detection models, etc.)
- Work with CBCT, MRI, and other DICOM imaging modalities
- Contribute to research papers, validation studies, and clinical trials
- Optimise models for inference (using tools like TensorRT, mixed precision, quantisation)
- Help set up cloud or local GPU infrastructure for model development
- Collaborate with regulatory and clinical teams to ensure AI models are safe, interpretable, and production-ready
Ideal Background:
- Strong experience building Deep Learning models from scratch (not just fine-tuning)
- Experience with medical imaging (CT, CBCT, MRI, Ultrasound, X-Ray, etc.)
- Solid understanding of 3D data, segmentation tasks, and landmark detection
- Hands-on with frameworks like PyTorch, TensorFlow, MONAI, nnU-Net, etc.
- Experience with DICOM data handling
- Bonus points for any exposure to regulatory standards (ISO 13485, FDA SaMD) or experience in clinical AI validation
- MSc, PhD, or equivalent industry experience in AI, Computer Vision, or Biomedical Engineering
Why Join?
- Help build something impactful from the ground up
- Work with internationally recognised AI talent
- Real-world clinical use cases with meaningful patient outcomes
- Remote flexibility, strong autonomy, high ownership culture
- Be part of a company that values intelligence, collaboration, and purpose-driven innovation
Science researcher employer: Impax Recruitment
Contact Detail:
Impax Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Science researcher
✨Tip Number 1
Familiarise yourself with the latest advancements in deep learning, particularly in medical imaging. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the field of AI and medical imaging. Attend relevant webinars, conferences, or local meetups to connect with potential colleagues and learn about industry trends that could give you an edge.
✨Tip Number 3
Showcase your hands-on experience with frameworks like PyTorch and TensorFlow by working on personal projects or contributing to open-source initiatives. This practical experience can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your understanding of regulatory standards in medical AI. Even if you don't have direct experience, demonstrating knowledge of ISO 13485 or FDA SaMD can highlight your commitment to safe and effective AI solutions.
We think you need these skills to ace Science researcher
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with deep learning models, particularly in medical imaging. Include specific projects or research that demonstrate your ability to build and optimise models from scratch.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI in healthcare and how your background aligns with the company's mission. Mention any relevant experience with DICOM data handling and regulatory standards, as these are key aspects of the role.
Showcase Relevant Projects: If you have worked on any projects involving 3D CNNs, segmentation models, or landmark detection, be sure to include these in your application. Provide links to your work or publications if possible, as this will strengthen your application.
Highlight Collaboration Skills: Since the role involves working closely with AI researchers, software engineers, and clinicians, emphasise your teamwork and communication skills. Share examples of how you've successfully collaborated in past projects to achieve common goals.
How to prepare for a job interview at Impax Recruitment
✨Showcase Your Deep Learning Expertise
Be prepared to discuss your experience in building deep learning models from scratch. Highlight specific projects where you've developed 3D CNNs or segmentation models, and be ready to explain the challenges you faced and how you overcame them.
✨Familiarise Yourself with Medical Imaging
Since the role involves working with various imaging modalities like CBCT and MRI, brush up on your knowledge of these technologies. Be ready to discuss how you've applied deep learning techniques to medical imaging in past projects.
✨Understand Regulatory Standards
If you have any experience with regulatory standards such as ISO 13485 or FDA SaMD, make sure to mention it. Even if you don't, showing an understanding of the importance of safety and interpretability in AI models will impress the interviewers.
✨Demonstrate Collaboration Skills
This role requires working closely with AI researchers, software engineers, and clinicians. Prepare examples of how you've successfully collaborated in multidisciplinary teams, focusing on communication and problem-solving skills.
