Kaiko is building a next-generation agentic clinical AI assistant that helps clinicians reason across patient data, guidelines, and diagnostics.

Healthcare decisions are rarely made by a single person or from a single data source. kaiko's assistant maintains longitudinal patient context across encounters, clinicians, and institutions, enabling collaboration, second opinions, and complex diagnostic workflows. The system is designed to operate safely in real clinical environments, with human oversight, auditability, and regulatory alignment at its core.

Our assistant core supports broadly applicable clinical tasks such as patient data navigation, guideline interaction, multimodal interaction (chat and voice), and care coordination. On top of this foundation, we are developing specialized diagnostic agents in areas such as oncology, radiology, and pathology.

We build in close collaboration with leading hospitals and research centers, including the Netherlands Cancer Institute (NKI). kaiko is a well-funded company with a growing international team, operating from Zurich and Amsterdam.

About the role

Our team is tackling the challenge of building a secure, scalable data and AI platform that can process massive, multimodal datasets for training cutting-edge MLLM models - while also handling sensitive medical data directly within hospital environments.

As a Data Platform Engineer, you'll design, deploy, maintain and optimize a self-hosted, open-source–driven infrastructure that powers data ingestion, transformation, and serving for both AI training pipelines and real-time hospital applications. Your primary focus will be on the platform itself - the infrastructure, tooling, and services that enable others to work with data effectively, rather than building data pipelines as an end goal. Your work will ensure that pipelines built on top of our platform are reproducible and compliant with strict privacy regulations.

This is a rare opportunity to shape a platform from the ground up - working with state-of-the-art tooling, solving unique scaling and security problems, and owning key technical decisions that will directly impact groundbreaking AI models and healthcare workflows.

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

• Design, maintain and own infrastructure and deployment for data exploration, transformation, and storage - including orchestration, containerization, and monitoring.

• Build and maintain internal platform services that abstract away complexity and promote self-serve data access and processing.

• Manage, deploy, and champion the use of standard open-source tooling and products around data platform and data engineering - including deploying and managing services via Helm charts and provisioning resources with Terraform.

• Design and implement data pipelines where needed, contributing to data and ML workflows as part of broader platform ownership.

• Collaborate with researchers, product teams, and other stakeholders to support their data needs.

About you

• 2-5 years of experience in production data platform, infrastructure, or platform engineering roles.

• Experience in providing in-house data orchestration services based on open-source software such as Dagster, Airflow or Prefect.

• Hands-on experience with infrastructure-as-code and container orchestration - particularly Terraform for resource provisioning and Helm for deploying services to Kubernetes.

• Proficiency with modern storage formats (e.g., Parquet, Delta, Iceberg) and object stores (e.g., S3, MinIO, Azure Blob).

• Solid programming skills in Python or another language suitable for data workflows (e.g., Scala or Java).

• Ability to thrive in a fast-paced, startup environment with a high degree of ownership.

Nice to have:

• Experience in AI/ML environment.

• Understanding of data standards in the medical domain, such as DICOM, FHIR, pathology slide images (Whole Slide Images).

• Knowledge of monitoring, logging, alerting and observability tools (e.g. Prometheus, Grafana, ELK Stack or Datadog).

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:

• An attractive and competitive salary, a good pension plan and 25 vacation days per year.

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

• A EUR 1000 learning and development budget to help you grow.

• Autonomy to do your work the way that works best for you, whether you have a kid or prefer early mornings.

• An annual commuting subsidy.

Our interview process

Our interview process is designed to assess mutual fit across skills, motivation, and values. It typically includes the following steps:

• Screening call: A short conversation to align on your motivation, career goals, and initial fit for the role.

• Coding assessment & debrief: You'll complete a time-limited coding exercise, started at a time of your choosing. Afterwards, you'll join a live session with members of our team to walk through your solution, explain your reasoning, and discuss any trade-offs you made.

• Technical interview: A deep dive into your problem-solving approach through a technical challenge, case study, or role-specific scenario.

• Onsite meeting (optional): You’ll meet team members across functions to explore collaboration dynamics, team fit, and day-to-day context.

• Final executive conversation: A discussion with a member of the executive team focused on long-term alignment, cultural fit, and shared expectations for impact.