About kaiko

Delivering high quality cancer care is complex; specialists form a view of each patient's condition by reasoning across different data - CT scans, genomics context, treatment history and clinical notes.

Current AI is powerful within domains but falls short when it comes to reasoning across data or domain areas. kaiko.w, our AI assistant for oncology, aims to equip every clinician with a full understanding of their patients, helping them to reason across data as they assess each case.

We’re building this in close collaboration with the Netherlands Cancer Institute (NKI) and a growing network of hospitals and research centers. We’ve raised significant long-term funding and have nearly doubled our team over the past year. We’re now 80+ people representing 25 nationalities, based across our offices in Zurich and Amsterdam

About the role

• This internship offers a hands-on opportunity to contribute to cutting-edge machine learning research in the healthcare domain. The intern will join Kaiko’s ML Research Team, working under the guidance of the ML Research Lead.

• Interns will collaborate with researchers, engineers, and data scientists across the company to implement state-of-the-art model architectures, gaining exposure to the challenges and impact of building AI solutions at scale.

• This is a 3–6-month hybrid internship, with an expectation of 2–3 days per week in-office.

• It’s an exciting opportunity for students or recent graduates passionate about machine learning to work on real-world problems using very large-scale, high-impact medical data—with strong mentorship and learning potential.

You will be based in either The Netherlands or Switzerland. with the expectation of spending at least 50% of your time at the office.

Some areas of responsibility

• You will focus on developing and evaluating image encoder models, specifically training architectures such as DINOv3 on large-scale medical imaging datasets to improve an existing model baseline.

About you

• Strong proficiency in Python, with hands-on experience using PyTorch for deep learning

• Solid understanding of deep learning fundamentals, especially in computer vision

• Experience training and evaluating image encoder models

• Familiarity with self-supervised learning techniques, particularly in visual representation learning

• Ability to read, interpret, and reproduce ML research papers, including recent advances in model architecture or training strategies

Nice to have:

• Familiarity with Vision Transformers (ViT) or similar modern architectures

• Exposure to large-scale training frameworks (e.g., PyTorch Lightning, HuggingFace, or distributed training setups)

• Experience with medical imaging datasets or healthcare-related ML applications

• Knowledge of experiment tracking tools like Weights & Biases or MLflow

• Comfort working in a Unix-based development environment, including use of Git and shell scripting

We are excited to gather a broad range of perspectives in our team, as we believe it will help us build better products to support a broader set of people. If you’re excited about us but don’t fit every single qualification, we still encourage you to apply: we’ve had incredible team members join us who didn’t check every box!

Why kaiko
At kaiko, we believe the best ideas come from collaboration, ownership and ambition. We’ve built a team of international experts where your work has direct impact. Here’s what we value:

• Ownership: You’ll have the autonomy to set your own goals, make critical decisions, and see the direct impact of your work.

• Collaboration: You’ll have to approach disagreement with curiosity, build on common ground and create solutions together.

• Ambition: You’ll be surrounded by people who set high standards for themselves and others, who see obstacles as opportunities, and who are relentless in their work to create better outcomes for patients.

 
In addition, we offer:

• A fair internship compensation.

• Great offsites and team events to strengthen the team and celebrate successes together.

• An annual commuting subsidy.