Medical AI Data Engineer 80-100 %

Key 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, ranging from backend services, APIs, and query orchestration pipelines to user-facing interfaces for clinical researchers and data scientists.

Required Qualifications

• AI/LLM Systems: Hands-on experience building production RAG pipelines and agentic AI workflows applied to clinical data querying.
• Medical Ontologies: Practical experience integrating a broad range of clinical ontologies and vocabularies into a query or data harmonization system, including SNOMED CT, ATC, RxNorm, ICD-10, LOINC, and OMOP standard vocabularies. Must demonstrate hands-on implementation experience managing concept mappings, hierarchies, and cross-ontology relationships.
• Backend Engineering: Solid Go development experience, including API design and data pipeline construction.
• Frontend Engineering: React development experience, including component design and building secure, clinical-facing user interfaces.
• SQL Expertise: Strong ability to write and optimize complex queries, as well as investigate, monitor, and troubleshoot the performance of production databases such as PostgreSQL and Oracle.

Preferred Qualifications

• Experience working with relational databases and complex clinical data models.
• Proven ability to build and maintain production React interfaces, ideally within a data-intensive or analytics-focused application.
• Prior experience working within privacy-preserving, federated, or distributed data environments where data cannot be moved or revealed in raw form. Understanding of the engineering constraints this imposes on query design and data access is essential.
• Sustained deep contributions to a single, specialized system over an extended period (18 months or more), given the complexity and proprietary nature of the platform.

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 Europe’s Health Valley.
• Impact: In a team of fewer than 20, you are not just a ticket-solver; you are building the foundation the company stands on.
• 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.

Who This Role Is Right For

• Individuals who thrive working across the full stack, from ontology mappings and data models to APIs and user interfaces.
• Candidates with 0-2 years working within a privacy-preserving or federated health data environment.
• Professionals with a strong instinct for data quality and semantic correctness, especially in a clinical or medical context.
• Those who appreciate challenging themselves out of their comfort zone and enjoy change.
• Individuals comfortable taking responsibility and being accountable for outcomes.
• Team players who stay humble and open to feedback, especially when working with world-class experts.