Medical AI Data Engineer

Tune Insight SA

EPFL Innovation Park

Bâtiment C

1015 Lausanne

Switzerland

Medical AI Data Engineer @ Tune Insight, Lausanne, Switzerland (On-site/Hybrid)

About Us

Tune Insights vision is to replace the data economy with an insight economy without compromising security and privacy. Raw sensitive data doesnt move and is never revealed, enabling companies to collaborate and valorize their data and models while keeping control of their value. Tune Insights B2B encrypted computing platform is dedicated to clinical and pharmaceutical research, bridging the gap between patient data and life science breakthroughs.

The Role

We are looking for an AI Data Engineer with solid engineering skills and a thorough understanding of medical questions. This role sits at the intersection of applied AI engineering, healthcare informatics, and full-stack software development. The ideal candidate will have directly contributed to query systems over federated health data, and will bring deep, hands-on familiarity with international medical interoperability standards and ontologies - not as theoretical knowledge, but as implemented, production experience.

Why Join Us?

- Meaningful Work: Your contribution to the infrastructure enables secure access to insights of patient data and enables life-saving research.

- Lausanne Hub: Work in the center of Europes Health Valley.

- Impact: In a team of <20>

- Challenging Problems: If you enjoy working at the boundary of AI, regulated health data, and distributed systems, you will not run out of hard problems here.

This role is right for you if you:

- Thrive working across the full stack: from ontology mappings and data models to APIs to user interfaces

- 0-2 years working within a privacy-preserving or federated health data environment.

- Have a strong instinct for data quality and semantic correctness, especially in a clinical or medical context

- Appreciate challenging yourself out of your comfort zone and enjoy change.

- Are comfortable taking responsibility and being accountable for outcomes.

- Stay humble and open to feedback, especially when working with world-class experts.

Requirements

- Hands-on experience building production RAG pipelines and agentic AI workflows applied to clinical data querying.

- Practical experience integrating a broad range of clinical ontologies and vocabularies into a query or data harmonization system, including but not limited to SNOMED CT, ATC, RxNorm, ICD-10, LOINC, and OMOP standard vocabularies.

- Hands-on implementation experience managing concept mappings, hierarchies, and cross-ontology relationships.

- Solid Go development experience, including API design and data pipeline construction.

- React development experience, including component design and building secure, clinical-facing user interfaces.

- Strong SQL expertise: writing and optimizing complex queries and investigating, monitoring, and troubleshooting the performance of production databases such as PostgreSQL and Oracle.

Responsibilities

- Design, develop, and maintain AI agents leveraging RAG and autonomous AI agents, enabling clinical researchers to query sensitive federated datasets using natural language.

- Build and iterate on an interoperable query language to SQL generation pipeline, including schema-awareness layers and query validation tailored to complex clinical data models.

- Implement and maintain data harmonization pipelines conforming to the OMOP Common Data Model and HL7 FHIR standard, enabling interoperability across heterogeneous clinical datasets from multiple institutions.

- Integrate and maintain medical ontology mappings across ICD-10, SNOMED CT, ATC, and RxNorm to ensure semantic consistency in federated patient data queries.

- Develop and maintain full-stack platform features: from backend services, APIs, and query orchestration pipelines through to user-facing interfaces for clinical researchers and data scientists.