Applied Research Engineer (Not Specified)

Applied Research Engineer

A variety of soft skills and experience may be required for the following role Please ensure you check the overview below carefully.

London (Hybrid)

We're building a foundational intelligence platform that helps organisations turn fragmented information into durable institutional knowledge. Enabling better reasoning, faster decisions, and preserved context over time.

Our initial focus is private capital, where information is complex and decisions are high stakes. Longer term, we're building core intelligence infrastructure for any information-led industry.

This is not a chat interface on top of documents or workflow automation. We're building the intelligence layer underneath.

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
• Developing information extraction pipelines for noisy enterprise documents
• 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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