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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 ML Engineer, RegBrain, your mission is to:

• Participate in the continuous improvement of RegBrain’s products.
• Develop advanced NLP and AI-based products that will delight users.
• Provide excellence in cloud-based ML engineering, with as much focus on Operations as Development.
• Expand of the Team’s knowledge via demonstration and documentation.
  
  

• Develop optimal ML & NLP solutions for RegBrain use cases, from baseline to SOTA approaches, wherever appropriate.
• Produce high quality, modular code, and deploy following our established DevOps CI/CD and best practices.
• Improve the efficiency, performance, and scalability of ML & NLP models (this includes data quality, ingestion, loading, cleaning, and processing).
• Stay up-to-date with ML & NLP research, and experiment with new models and techniques.
• Perform code-reviews for your colleague’s code. Engage with them to raise standards of Software engineering.
• Propose cloud architectures for ML-based products that need new infrastructure.
• Participate in the monitoring and continuous improvement of existing ML systems.
  
  

• Experience analyzing large volumes of textual data (almost all of our use cases will involve NLP).
• Ability to write clear, robust, and testable code, especially in Python.
• Familiarity with SQL and NoSQL/graph databases.
• Extensive experience with ML & DL platforms, frameworks, and libraries.
• Extensive experience with end-to-end model design and deployment within cloud environments.
• A systems thinking approach, with passion for MLOps best practises.
• An engineer that can think in O(n) as much as plan the orchestration of their product.
• Solid understanding of data structures, data modelling, and software architecture, especially cloud-based.
• An engineer that can keep up with mathematically and statistically-oriented colleagues.
• A healthy sense of humour.
  
  

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  IT Services and IT Consulting

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