ML Engineer Offline

As an ML Engineer, you'll work at the intersection of machine learning systems, knowledge representation, and reasoning infrastructure — building the foundational intelligence layer that powers our platform. This isn't about wrapping APIs or fine-tuning models in isolation. You'll be solving hard problems at the frontier of applied ML.

What you'll work on:

Designing ML pipelines that transform unstructured, proprietary information into semantically rich, queryable representations

Building and integrating models that enable probabilistic reasoning, multi-hop inference, and context-aware decision support

Developing agentic workflows where AI assists human judgment through explainable outputs, uncertainty quantification, and iterative feedback loops

Defining evaluation metrics and testing frameworks for high-stakes, information-led environments

Contributing to architectural decisions around ML infrastructure and model serving

Collaborating closely with backend engineers, product, and domain experts

What We're Looking For

Strong foundations in machine learning — NLP, information extraction, representation learning

Systems thinking mindset, not just model performance in isolation

Comfortable working in ambiguity and defining problems from first principles

Intellectually honest — willing to challenge assumptions, including our own

Pragmatic: ships working systems, iterates on real feedback, avoids over-engineering

Cares about reproducibility, clarity, and long-term maintainability

What We Offer

Work on genuinely hard, foundational problems at the intersection of ML and knowledge systems

Founder-led, deeply technical environment — low-ego, high-ownership

Fast feedback loops, strong opinions, high bar for clarity

Flexible/hybrid setup centred around London

Early-stage equity

Our Values

First-principles thinking. Human judgment matters. Intellectual honesty. Trust by default. Compounding advantage. Build infrastructure, not wrappers.