Founding Applied Research Engineer | AI & Knowledge Systems in London

What if you could work on the part of AI that sits underneath the application layer?

Join a stealth, early-stage AI company building foundational technology for how organisations structure, retrieve and reason over complex information.

They’re tackling a problem that becomes increasingly important as organisations adopt AI: having access to more information isn’t enough. The real challenge is understanding relationships, preserving context and being able to reason reliably across large volumes of messy, changing data.

We are backed by strong technical talent and is building a small, highly capable team around this problem. They’re now looking for an Applied Research Engineer to take ownership of the systems that sit at the core of the platform.

This is an opportunity to work somewhere you can take research ideas, turn them into production systems and have a direct influence on the technical direction of the company.

What you’ll get to work on

• You’ll design and build retrieval systems that go beyond basic semantic search, combining dense, sparse and structured approaches
• You’ll explore how information can be represented so that AI systems can reason over entities, relationships and context
• You’ll build information extraction systems capable of dealing with ambiguous, messy and unstructured data
• You’ll work on entity recognition, relationship extraction, coreference resolution and temporal understanding
• You’ll develop approaches for maintaining and evolving knowledge as new information becomes available
• You’ll work on provenance, validation and conflict resolution so information can be trusted
• You’ll stay close to relevant research and translate useful ideas into production technology
• You’ll have significant influence over the architecture and technical direction of these systems

We’re looking for someone who

• Has research or industry experience in information retrieval, NLP, knowledge representation, knowledge engineering or a closely related field
• Has taken research beyond experimentation and into a real-world system
• Thinks deeply about how information should be structured to support reasoning
• Understands that retrieval is about more than simply finding the most similar documents
• Is comfortable working from first principles and defining problems without a predefined roadmap
• Enjoys working in a small team where you’ll have significant ownership
• Is pragmatic and cares about shipping useful systems rather than research for research’s sake

You could come from academia, an applied research team or a highly technical engineering environment . The key thing is the depth of your thinking and your ability to turn that thinking into something that works.

• Work on foundational AI problems: Get beyond the application layer and work on how AI systems actually understand and reason over information
• Research → production: See your ideas move from papers and experiments into systems used in the real world
• High ownership: You’ll have genuine responsibility for a core part of the technology
• Small technical team: Work closely with experienced founders and engineers without layers of management
• Technical influence: Your decisions will directly shape the architecture and direction of the platform
• Intellectual challenge: Work on difficult problems around retrieval, knowledge representation and reasoning that don’t have obvious answers
• Equity: Have meaningful ownership in an early-stage company

The environment

You’ll be joining a small, founder-led and deeply technical team where you’ll be expected to think independently, challenge assumptions and move quickly.

There isn’t a huge amount of process or bureaucracy. You’ll have a high degree of autonomy, but you’ll also have a high bar for technical thinking and execution.

The team works hybrid from London , with flexibility around how and where you work.

The company is currently operating in stealth , so further information about the product and customers will be shared during the interview process.

If you’re interested in NLP, retrieval, knowledge representation or reasoning and want to work on a genuinely foundational AI problem rather than another wrapper around an existing model, I’d be happy to tell you more.

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