Lead the development and scaling of our scientific knowledge graph—ingesting, structuring, and enriching massive datasets from research literature and global data sources into meaningful, AI-ready insights.

Requirements:

- Strong experience with knowledge graph design and implementation (Neo4j, RDFLib, GraphQL, etc.).

- Advanced Python for data engineering, ETL, and entity processing (Spark/Dask/Polars).

- Proven track record with large dataset ingestion (tens of millions of records).

- Familiarity with life-science or biomedical data (ontologies, research metadata, entity linking).

- Experience with Airflow/Dagster/dbt, and data APIs (OpenAlex, ORCID, PubMed).

- Strong sense of ownership, precision, and delivery mindset. Nice to Have:

- Domain knowledge in life sciences, biomedical research, or related data models.

- Experience integrating vector/semantic embeddings (Pinecone, FAISS, Weaviate).

We offer:

• Attractive financial package

• Challenging projects

• Professional & career growth

• Great atmosphere in a friendly small team