The Role
As an Applied Research Engineer, you’ll own the core knowledge and retrieval infrastructure that everything else builds on.
You’ll work at the intersection of knowledge representation, information retrieval, and applied NLP, translating research ideas into production systems that reason over real‑world, proprietary data.
This is a high‑ownership role in a small, deeply technical team.
What You’ll Work On
- Designing knowledge models and ontologies that preserve semantic meaning and relationships
- Building hybrid retrieval systems (graph, structured, sparse, dense) with contextual understanding and re‑ranking
- Managing knowledge quality and evolution (provenance, conflict resolution, schema changes)
- Turning research into robust, production grade infrastructure
What We’re Looking For
We’re especially interested in candidates with experience in one or more of the following areas:
- Graph databases and graph based systems (ideally Neo4j or similar)
- Information retrieval systems, including search, ranking and semantic retrieval
- Enterprise cloud environments, particularly Azure and operating production systems at scale
In addition, you’ll likely have:
- A strong background in IR, NLP, knowledge representation, or a related field
- Experience shipping real systems, not just prototypes or research code
- Careful thinking around how information should be structured to support reasoning
- Comfort working from first principles in an early‑stage environment
- A high sense of ownership and responsibility for core systems
This Role Is a Good Fit If You…
- Care about the architecture of knowledge, not just model performance
- Enjoy building systems that genuinely understand context, not just embeddings
- Want to influence the technical direction of a core platform
- Value intellectual honesty and pragmatic decision making
- Prefer foundational infrastructure over surface level AI features
The Role
As an Applied Research Engineer, you’ll own the core knowledge and retrieval infrastructure that everything else builds on.
You’ll work at the intersection of knowledge representation, information retrieval, and applied NLP, translating research ideas into production systems that reason over real‑world, proprietary data.
This is a high‑ownership role in a small, deeply technical team.
What You’ll Work On
- Designing knowledge models and ontologies that preserve semantic meaning and relationships
- Building hybrid retrieval systems (graph, structured, sparse, dense) with contextual understanding and re‑ranking
- Managing knowledge quality and evolution (provenance, conflict resolution, schema changes)
- Turning research into robust, production grade infrastructure
What We’re Looking For
We’re especially interested in candidates with experience in one or more of the following areas:
- Graph databases and graph based systems (ideally Neo4j or similar)
- Information retrieval systems, including search, ranking and semantic retrieval
- Enterprise cloud environments, particularly Azure and operating production systems at scale
In addition, you’ll likely have:
- A strong background in IR, NLP, knowledge representation, or a related field
- Experience shipping real systems, not just prototypes or research code
- Careful thinking around how information should be structured to support reasoning
- Comfort working from first principles in an early‑stage environment
- A high sense of ownership and responsibility for core systems
This Role Is a Good Fit If You…
- Care about the architecture of knowledge, not just model performance
- Enjoy building systems that genuinely understand context, not just embeddings
- Want to influence the technical direction of a core platform
- Value intellectual honesty and pragmatic decision making
- Prefer foundational infrastructure over surface level AI features
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