Key Responsibilities

Design, develop, and maintain AI agents leveraging RAG and autonomous AI technologies, empowering 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 that conform to the OMOP Common Data Model and HL7 FHIR standard, facilitating interoperability across diverse 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 and APIs to query orchestration pipelines and user-facing interfaces tailored for clinical researchers and data scientists.

What We Are Looking For

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 but not limited to SNOMED CT, ATC, RxNorm, ICD-10, LOINC, and OMOP standard vocabularies. Experience managing concept mappings, hierarchies, and cross-ontology relationships is essential.
Backend Engineering: Solid Go development experience, particularly in API design and data pipeline construction.
Frontend Engineering: Experience with React development, including component design and the creation of secure, clinical-facing user interfaces.
Strong SQL Expertise: Proficient in writing and optimizing complex queries, as well as investigating, monitoring, and troubleshooting the performance of production databases such as PostgreSQL and Oracle.

Experience with relational databases and complex clinical data models.
Proven ability to build and maintain production React interfaces, ideally within data-intensive or analytics-focused applications.
Prior experience in privacy-preserving, federated, or distributed data environments where data cannot be moved or revealed in raw form is strongly preferred. Understanding the engineering constraints this imposes on query design and data access is essential.
We highly value candidates who have made sustained, deep contributions to a single, specialized system for an extended period (18 months or more).

Meaningful Work: Your contributions will enhance the infrastructure that enables secure access to patient data insights and supports life-saving research.
Lausanne Hub: Work in the heart of Europe’s Health Valley.
Impact: With a team of fewer than 20, your role is pivotal; you will help lay the foundation for our company's success.
Challenging Problems: Engage with cutting-edge challenges at the intersection of AI, regulated health data, and distributed systems.

Thrive in working across the full stack, from ontology mappings and data models to APIs and user interfaces.
Have 0-2 years of experience in a privacy-preserving or federated health data environment.
Possess a strong instinct for data quality and semantic correctness, particularly in clinical or medical contexts.
Welcome challenges that push you out of your comfort zone and embrace change.
Are comfortable taking responsibility and holding yourself accountable for outcomes.
Stay humble and open to feedback, especially when collaborating with world-class experts.

Apply online using the form below. Only applications matching the job profile will be considered.

Work locationLausanne, Switzerland