Machine Learning Engineer

ML Engineer

(Stealth AI Company)

About the company

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.

We are starting with information‑dense, judgment‑heavy industries where decision‑making under uncertainty is core. 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.

The problem we’re solving

Most organisations don’t struggle with data volume. They struggle with:

fragmented information across systems and time

loss of context and institutional memory

repeated manual synthesis

knowledge walking out the door

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.

What we build

We build software that helps organisations understand their own information, not just store or search it.

The platform:

ingests internal and external data

structures information to preserve meaning, relationships, and provenance

enables reasoning across time, sources, and uncertainty

keeps humans in the loop where judgment matters

evolves as organisational knowledge evolves

We are intentionally not:

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:

How can organisations reason reliably over their own information at scale?

The role

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.

This is not a model‑tuning or API‑wrapping role. You’ll tackle foundational problems such as:

Knowledge extraction & structuring

Designing ML pipelines that turn unstructured, proprietary data into semantically rich representations.

Reasoning systems

Building and integrating models that support probabilistic reasoning, multi‑hop inference, and context‑aware decision support.

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.

What you’ll be expected to do

Design, train, and deploy ML models that handle real‑world complexity: noise, ambiguity, evolving schemas

Think deeply about information representation, not just retrieval or ranking

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

What we’re looking for

Strong foundations in machine learning (e.g. NLP, information extraction, representation learning)

Systems‑oriented mindset — performance in production matters more than benchmarks

Comfort working in ambiguity and defining problems from first principles

Intellectual honesty and willingness to challenge assumptions

Motivation to build infrastructure that compounds in value over time

Nice to have

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

Financial experience or interest in finance.

Previous start up experience.

Working environment

Based in London

In‑office by default with work from home on Wednesdays

Founder‑led, deeply technical and substance‑driven

Low‑ego, high‑ownership culture

Strong opinions, fast feedback loops and a high bar for clarity

Minimal ceremony, maximum focus on building durable systems.

Values

First‑principles thinking — design from fundamentals

Human judgment matters — AI supports decisions, it doesn’t replace responsibility

Intellectual honesty — correctness over hype

Trust by default — security, provenance, and explainability built in

Compounding advantage — systems that get better over time

Build foundations, not wrappers — infrastructure over surface features