AI Engineer in London

CUBE are a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients.

Why us? CUBE is a globally recognized brand at the forefront of Regulatory Technology. Our industry‐leading SaaS solutions are trusted by the world's top financial institutions globally. In 2024, we achieved over 50% growth, both organically and through two strategic acquisitions. We’re a fast‐paced, high‐performing team that thrives on pushing boundaries—continuously evolving our products, services, and operations. At CUBE, we don’t just keep up we stay ahead.

We believe our future is built by bold, ambitious individuals who are driven to make a real difference. Our "make it happen" culture empowers you to take ownership of your career and accelerate your personal and professional development from day one. With over 700 CUBERs across 19 countries spanning EMEA, the Americas, and APAC, we operate as one team with a shared mission to transform regulatory compliance. Diversity, collaboration, and purpose are the heartbeat of our success.

We were among the first to harness the power of AI in regulatory intelligence, and we continue to lead with our cutting‐edge technology. At CUBE, you will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that are reshaping the world of regulatory compliance.

Role Overview

As a Machine Learning Engineer, you will drive the development and continuous improvement of our AI/ML‐powered products, including recommender systems, NLP solutions, and LLM‐driven features. Your mission is to deliver advanced, production‐ready ML solutions that delight users while maintaining excellence in cloud‐based ML engineering with equal focus on Development and Operations.

Key Responsibilities

• Develop, improve and productionise ML and LLM‐based features for our internal and external platforms to solve challenges like:

• High cardinality hierarchical classifications
• Personalised content filtering of legal documents
• Using ML/LLMs to network heterogenous text data at scale
• Building agent‐ready tools to leverage insights from our knowledge graph

Requirements

Experience & Technical Skills:

• At least two years of professional experience in ML, ideally within the NLP domain.
• Solid understanding of statistical and machine learning techniques with ability to select models that best suit the problem at hand.
• Ability to write clear, robust, and testable code in Python.
• Confident using SQL and NoSQL/graph databases.
• Solid understanding of data structures, data modelling, and software architecture, especially cloud‐based.

Mindset & Approach:

• A systems thinking approach with ability to think in O(n) as much as plan product orchestration.
• An engineer who can keep up with mathematically and statistically‐oriented colleagues.
• Natural creativity with a track record of exploring innovative use cases of data and applications of statistical/ML methods.
• Strong communication skills, capable of explaining complex technical concepts to employees across the business.

Desirable:

• Experience building and deploying LLM‐based agentic solutions into production.
• Solid understanding of recommender system principles and how they work.
• Demonstrated proficiency through GitHub profiles/online portfolios, Stack Overflow or Kaggle contributions.
• Experience or interest in neurosymbolic AI.
• A healthy sense of humour, or short of this, a high weekly rate of Machine Learning puns.

Interested?

If you are passionate about leveraging technology to transform regulatory compliance and meet the qualifications outlined above, we invite you to apply. Please submit your resume detailing your relevant experience and interest in CUBE.

CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.