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Salary: USD 140,000 - 175,000 / annual
BioAge Labs is a clinical-stage biopharmaceutical company developing therapeutics for metabolic diseases by targeting the biology of human aging. The company uses a proprietary discovery platform built on human longevity data to identify drug targets and mechanisms of action.
You will design, build, and maintain scalable knowledge graph solutions that harmonize longitudinal human data, genetic evidence, animal models, structured ontologies, and curated external knowledge. Your work will translate ontologies and semantic models into production systems, develop pipelines connecting enterprise data to graph platforms, and build queries supporting downstream analytics and intelligent applications.
Key responsibilities include:
- Maintain and extend the knowledge graph, including schema, ingestion pipelines, and query layer (Neo4j, Cypher, Apache Jena Fuseki, BigQuery on Google Cloud)
- Build and run validation and data-governance pipelines to maintain data quality, consistency, and performance, covering schema checks, constraint validation, identifier reconciliation, provenance, and versioning
- Maintain ontologies and semantic models with biology and data science teams; develop mapping tools for scientists to annotate new terms and analyses
- Integrate knowledge graph solutions with cloud platforms and enterprise data ecosystems, including Google Cloud and BigQuery
- Collaborate with data engineers, data scientists, AI teams, and biologists to make graph data accessible and useful for downstream applications
- Write clean, testable, production-grade code and apply engineering best practices throughout development and delivery
You will own solutions from architecture and technology selection through implementation, testing, and performance optimization, working closely with cross-functional teams to turn business requirements into reliable systems.
REQUIREMENTS:
Basic Qualifications:
- Bachelor's degree or higher in computer science, data engineering, AI, or related field
- 3+ years of industry experience in data engineering
- Experience working with biological or biomedical data (genomics, proteomics, clinical, phenotype data)
- Familiarity with standard biomedical ontologies and identifiers (UniProt, Gene Ontology, EFO, MONDO)
Technical Qualifications:
- Hands-on experience with graph databases (Neo4j, Stardog, GraphDB) including writing and optimizing queries in Cypher or SPARQL
- Working knowledge of semantic modeling concepts and standards (RDF, OWL); ability to turn ontology into working schema
- Experience designing and building APIs (REST, GraphQL using frameworks like FastAPI) for graph data access
- Experience building data-quality or validation frameworks
- Familiarity with Google Cloud and BigQuery
- Strong Python and working knowledge of JavaScript for data pipelines, graph integrations, and production applications
- Strong software engineering practices (data modeling, system design, testing, documentation, version control)
- Preferred: Experience with LLM or agent systems
- Preferred: Experience with Open Targets and other drug-discovery knowledge bases
Collaboration and Ownership:
- Strong problem-solving skills and sound technical judgment when requirements evolve or design priorities compete
- Ability to take ownership of solutions from initial design through implementation and ongoing maintenance
- Clear written and verbal communication; ability to explain architecture decisions and technical trade-offs to clients and stakeholders
- Ability to work effectively across data engineering, biology, and AI teams
