About the job
Mission
At Neko Health, we are transforming the healthcare landscape by prioritizing prevention over treatment. Utilizing innovative, non-invasive technology combined with clinical expertise, we provide early, actionable health insights.
AI Intern – Data & AI Team (Summer Internship)
Location: Stockholm (on-site)
Duration: Summer Internship (approximately 8–12 weeks)
Team: Data & AI
About Neko Health
Neko Health is at the forefront of preventive healthcare, leveraging advanced diagnostics, multimodal data, and cutting-edge artificial intelligence. Our aim is to enhance long-term health outcomes by facilitating earlier detection and a more profound understanding of disease risk. By integrating engineering, medical science, and machine learning, we create systems that convert complex health data into actionable insights.
About the Role
We are seeking a passionate AI Intern to join our Data & AI Team this summer. This internship will provide you with the opportunity to work directly with real-world health data and contribute to impactful early-detection solutions.
As an AI Intern, you will closely collaborate with researchers, engineers, and clinicians to investigate how multimodal health data can be modeled, analyzed, and transformed into valuable preventive insights. You will function within Neko’s scientific and technical frameworks to ensure rigor, reproducibility, and measurable impact. Each intern is assigned a dedicated mentor and entrusted with a technically challenging project that aligns with Neko’s mission.
What You’ll Work On
Potential projects may include:
Developing machine learning models for multimodal health data (imaging, physiological signals, structured clinical data, etc.)
Applying computer vision techniques to medical imaging or sensor-based data
Experimenting with foundation models and representation learning approaches
Prototyping data-centric workflows for pre-processing, labeling, and evaluation
Designing and validating algorithms for early-detection insights
Evaluating model performance, robustness, and generalization in healthcare contexts.

