Senior Deep Learning Engineer – Medical Imaging
Medical AI | Retinal Image Analysis | Technical Project Leadership
About the Company
SciPo is supporting an established medical AI organisation specialising in artificial intelligence solutions for retinal image analysis and ophthalmic diagnostics.
The business develops deep-learning technologies that support retinal screening and clinical decision-making, helping advance the application of AI within eye care.
Its medical AI capabilities include the automated analysis of colour fundus images to support the diagnosis of conditions such as diabetic retinopathy, age-related macular degeneration and glaucoma.
The successful candidate will join a multidisciplinary Medical AI team, working across deep learning algorithm development, software engineering, algorithm deployment, product development and quality and regulatory disciplines.
The Role
We are looking for a Senior Deep Learning Engineer to act as a technical point of contact for deep learning and lead selected AI development projects from initial opportunity and requirements through to experimentation, validation, release and handover.
This is a hands-on technical leadership position, combining practical model development with project ownership and stakeholder coordination.
You will remain closely involved in the code while providing technical direction, creating clarity around project requirements and working collaboratively with colleagues across engineering, product, clinical and quality functions.
Key Responsibilities
- Lead deep-learning projects, coordinating scope, priorities, planning, risks and technical decisions with relevant stakeholders.- Translate clinical, customer and product requirements into clear AI requirements and research questions.- Design, implement and evaluate deep-learning models for medical image analysis, primarily using Python and TensorFlow.- Determine and manage the technical content of algorithm releases, communicating progress, performance and technical trade-offs.- Build reproducible and scalable training and evaluation workflows in collaboration with Deep Learning and DevOps engineers.- Use and improve Kubeflow-based pipelines where appropriate.- Guide and execute experiments covering dataset selection, preprocessing, model training, clinical validation, error analysis and performance assessment.- Provide technical direction to other engineers through design discussions, code reviews, knowledge sharing and mentoring.- Support the supervision of interns where required.- Document algorithms, design decisions, results and risks to support product development, quality assurance and regulatory certification activities.- Identify and address potential product safety, information security and privacy issues in accordance with company policies and procedures.- Evaluate relevant developments in deep learning, computer vision and MLOps, translating promising approaches into practical improvements.
Essential Requirements
- Master's degree or PhD in Computer Science, Artificial Intelligence, Biomedical Engineering, Electrical Engineering, Data Science or a closely related discipline.- At least 3 years of relevant professional or applied research experience in deep learning, machine learning, computer vision or AI product development.- Strong Python programming skills.- Practical experience developing deep-learning solutions using TensorFlow and/or PyTorch.- Experience leading technical projects, owning deliverables and working effectively with multidisciplinary stakeholders, including Product Management, Software Engineering, DevOps, clinical experts and Quality Assurance and Regulatory Affairs (QARA).- Experience taking machine learning models beyond experimental notebooks, including structured experimentation, testing, review, documentation, reproducibility and production release readiness.- Strong analytical and troubleshooting capabilities, with the ability to balance research quality, product requirements and delivery constraints.- Clear written and verbal English communication skills, including the ability to explain technical decisions and their impact to non-specialists.- A collaborative, pragmatic and improvement-oriented approach.
Desirable Experience
- Experience with Kubeflow or comparable MLOps and workflow orchestration tools.- Experience in medical imaging, preferably ophthalmic or retinal image analysis.- Experience working within regulated medical-device software environments.- Familiarity with design controls, technical documentation and quality management processes.- Experience working with large-scale image datasets, data management tooling and statistical evaluation.- Working knowledge of C++, Docker, Kubernetes, Git, cloud platforms or CI/CD pipelines.
Location and Eligibility
- Based in Nijmegen, Netherlands.- Regular in-person collaboration is an essential part of the role.- Candidates must hold EU citizenship and have the right to work in the Netherlands.- Visa sponsorship is not available.- Candidates must already live in the Netherlands or be willing to relocate.
Benefits and Practical Information
- 32–40 hours per week.- Flexible start and finish times.- 25 days annual leave, based on a 40-hour working week.- 8% holiday allowance.- 8% pension contribution, with a flexible pension plan.- Initial one-year employment contract, with the prospect of a permanent contract subject to probation.