About the Role:
We’re seeking an experienced Ontologist/Data Architect to help design and scale intelligent systems that model, reason over, and derive insights from complex structured knowledge. In this role, you'll bridge cutting-edge machine learning with symbolic reasoning, helping to build systems that understand and represent domain knowledge in a form usable by machines and humans alike.
You’ll work alongside a multidisciplinary team of engineers, data scientists, and ontologists to develop a scalable, semantically rich data infrastructure that powers search, recommendations, analytics, and automated decision-making.
Responsibilities:
- Design and implement scalable knowledge graph pipelines using ontologies, entity resolution, and relation extraction techniques.
- Integrate structured (e.g., databases) and unstructured data (e.g., text) into cohesive knowledge representations.
- Develop tools and services for knowledge ingestion, enrichment, reasoning, and querying (e.g., via SPARQL, Cypher).
- Apply machine learning and NLP techniques to extract, disambiguate, and classify entities and relationships from large datasets.
- Collaborate with ontology engineers to develop and extend domain ontologies using OWL, RDF, or SKOS.
- Build APIs and systems that expose knowledge graph capabilities to downstream applications.
- Optimize knowledge graph performance and scalability in distributed environments.
- Stay current with developments in symbolic AI, hybrid AI, and graph-based ML.
Basic Qualifications:
- Bachelor’s or Master’s in Computer Science, AI, Semantic Technologies, or related field.
- 5+ years of experience in ontology development and data architecture with AI/ML applications.
- Hands-on experience with knowledge graph technologies (e.g., RDF, OWL, SPARQL, Neo4j, TigerGraph).
- Proficiency in Python (or similar languages) and familiarity with software development best practices.
- Understanding of ontological modeling and reasoning principles.
- Experience building and deploying production-grade ML systems or graph pipelines.
Preferred Qualifications:
- Experience working with large-scale graph databases or triple stores.
- Familiarity with standards like W3C Semantic Web stack, SHACL, and schema.org.
- Knowledge of probabilistic reasoning, embeddings for graphs (e.g., node2vec, TransE), or GNNs.
- Experience with NLP and ML tools (e.g., spaCy, Hugging Face, Scikit-learn, PyTorch, TensorFlow).
- Experience in domain-specific knowledge modeling (e.g., healthcare, finance, scientific research).
- Familiarity with hybrid symbolic-neural architectures.
What We Offer:
- Competitive salary with equity options
- Flexible remote work environment
- Comprehensive benefits, including health, vision, and dental
- An opportunity to work at the forefront of AI and knowledge systems
- Learning and development support for ongoing career growth