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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 ambitious clinical tech company revolutionising AI in healthcare.
- Benefits: Enjoy remote work flexibility, autonomy, and a culture that values innovation.
- Why this job: Shape impactful AI solutions that improve patient outcomes and collaborate with top talent.
- Qualifications: Strong experience in building deep learning models and knowledge of medical imaging required.
- Other info: Opportunity for occasional travel to the UK and involvement in cutting-edge research.
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
Researcher - Science employer: Impax Recruitment
Contact Detail:
Impax Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Researcher - Science
✨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 AI and medical imaging fields. Attend relevant webinars or conferences 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 be a great talking point in interviews.
✨Tip Number 4
Research the company’s mission and values thoroughly. Understanding their focus on transforming clinical outcomes through AI will help you align your answers with their goals during the interview process.
We think you need these skills to ace Researcher - Science
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in deep learning and medical imaging. Focus on projects where you've built models from scratch, and include specific technologies like PyTorch or TensorFlow that you've used.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI in healthcare. Mention how your background aligns with the company's mission to transform clinical outcomes and provide examples of your work in 3D imaging or related fields.
Showcase Your Research Experience: If you've contributed to research papers or clinical trials, make sure to detail this in your application. Highlight any publications or studies that demonstrate your expertise in deep learning and medical imaging.
Highlight Collaboration Skills: Since the role involves working closely with various teams, emphasise your ability to collaborate effectively. Provide examples of past teamwork experiences, especially in interdisciplinary settings involving AI and healthcare professionals.
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 medical imaging, brush up on your knowledge of different modalities like CT, MRI, and DICOM data handling. Be ready to discuss how you've applied this knowledge in previous roles or 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 have direct experience, showing an understanding of these standards can set you apart.
✨Prepare for Technical Questions
Expect technical questions related to frameworks like PyTorch and TensorFlow. Be ready to demonstrate your problem-solving skills and how you would optimise models for inference using tools like TensorRT or mixed precision.
