Inżynier ds. Automatyzacji in London

We are building a foundational intelligence platform that transforms fragmented, proprietary information into durable institutional intelligence - enabling organisations to reason faster, preserve context, and compound knowledge over time.

Long-term, the platform is designed for any information-led organisation where trust, provenance, and context matter. Our focus is not surface-level AI features, but the intelligence substrate that workflows depend on. Most organisations don't struggle with data volume, but with repeated manual synthesis and AI tools that retrieve information but don't reason over it.

We are building the foundational layer beneath workflows: how information is structured, contextualised, and reasoned over. We build software that helps organisations understand their own information, not just store or search it.

• Ingests internal and external data
• A workflow automation tool
• A chat UI on top of documents
• A standalone 'knowledge graph product'

Graphs, ML, probabilistic reasoning, and human-in-the-loop systems are combined to solve a larger problem:

As an ML Engineer, you'll work at the intersection of machine learning systems, knowledge representation, and reasoning infrastructure - helping build the core intelligence layer of the platform.

• Knowledge extraction & structuring: Designing ML pipelines that turn unstructured, proprietary data into semantically rich representations.
• Agentic workflows: Developing systems where AI augments human judgment via explainability, uncertainty estimation, and feedback loops.
• Evaluation & reliability: Defining metrics and testing frameworks appropriate for high-stakes, information-led environments.
• Production integration: Working closely with backend engineers, product, and domain experts to ensure ML systems are robust and scalable.
• Design, train, and deploy ML models that handle real-world complexity: noise, ambiguity, evolving schemas.
• Contribute to architectural decisions around ML infrastructure and system design.
• Ship working systems, iterate based on feedback, and avoid over-engineering.
• Maintain a high bar for clarity, reproducibility, and long-term maintainability.

Strong foundations in machine learning (e.g. Systems-oriented mindset - performance in production matters more than benchmarks).

Experience with graph databases (preferably Neo4j). Background in information retrieval (search, ranking, semantic search, hybrid systems). Experience building or operating ML systems in enterprise cloud environments, particularly Azure.

In-office by default with work from home on Wednesdays. Minimal ceremony, maximum focus on building durable systems. Human judgment matters - AI supports decisions, it doesn't replace responsibility. Build foundations, not wrappers - infrastructure over surface features.