AI at CUBE

CUBE uses AI and NLP to machine read the regulatory internet, at global scale. We collect, clean, standardise, translate, monitor, classify, and enrich regulatory data across 180 countries in over 60 languages. All in near real-time.

We've even built our own ontology of regulation—machine-driven and continuously refined by a team of subject matter experts.

On a high level, CUBE uses AI to transform regulatory data into regulatory intelligence. 

RegTransform

CUBE RegTransform is an AI-powered service that is completely unique to CUBE. RegTransform powers CUBE technologies and is the critical component behind CUBE’s interface that enables effective and accurate regulatory data management.

RegTransform is the technological magic that transforms regulatory data into regulatory intelligence at a scale and quality not possible at a human level.

The Role

We are seeking for an experienced MLOps Engineer to join our newly formed DevOps/Cloud team while working closely with our established Data Science teams. This is a hands-on role, and you will actively build, deploy, enhance, and maintain ML models and pipelines. You would also need to provide insights to engineers on the performance of ML models, by implementing monitoring and feedback mechanisms. Other tasks can include undertaking DevOps and Cloud related tasks, such as enhancing CI/CD processes, building Azure infrastructure with Terraform, or optimizing deployment with Kubernetes, etc. and you will be able to draw and learn from the experience pool of the DevOps team.

Responsibilities: 

• Design and implement best practices for ML data pipelines on Azure cloud.
• Collaborate cross-teams, including AI engineers, developers, DevOps engineers, infrastructure engineers and product owners to ensure that the solutions are efficient, scalable, secure, and meet business objectives.

• Collaborate with data scientists and software engineers to create robust data pipelines and build ML workflows.

• Design and implement monitoring and alerting systems for production ML systems.

• Create and present solutions for MLOps enhancements, green field builds to the ML teams and DevOps lead.

• Work closely with DevOps teams to ensure smooth integration of ML systems with existing infrastructure.

• Continuously evaluate and improve ML processes, ensuring the highest level of accuracy and efficiency

• Automate processes to optimize model training and deployment, while minimizing human error and cost.

• Create and maintain documentation for ML workflows and processes.

• Develop and implement security and compliance measures to ensure the confidentiality and integrity of sensitive data.

• Propose, build, and maintain best DevOps practises in CI/CD, cloud, and security.
• Stay up to date with emerging trends and technologies in Kubernetes, Containerization, Azure and MLOps practises, and continuously improve ML systems and processes accordingly.

About You

Required Qualifications: 

• BSc/MSc degree in Computer Science, Software Engineering, or a related field.

• Hands-on experience as a Senior Software Engineer or DevOps/Cloud Engineer worked in ML domain and skilled in MLOps design and implementation.

• Good level of understanding of Cloud and DevOps and latest technologies.

• Extensive knowledge of ML concepts and frameworks (such as TensorFlow, PyTorch, or Scikit-Learn) and demonstrated experience in implementing data pipelines, and model deployment.
• Good understanding and experience with containerization and orchestration technologies such as Docker and Kubernetes, and networking (we use Azure Kubernetes Service extensively).
• Solid programming or scripting experience in languages such as Python, Java, C++, and keen to automate processes as much as possible.
• Good understanding and experience with CI/CD process and tools (we use Azure DevOps).
• Experience with database systems such as SSMS, MySQL, PostgreSQL, or MongoDB
• Experienced with cloud-based infrastructure, such as AWS, Azure, or GCP (we are an Azure shop).

• Have the mentality of automation and optimisation and have necessary scripting skill to implement it.

• Self-motivated organised professional, who takes responsibility for their work, capable of being highly productive in a home-based work environment.

Preferred Qualifications: 

• Advanced degree (e.g., master’s or PhD) in Computer Science, Machine Learning, or a related field.

• Proven experience in building and deploying ML products within cloud environments.
• Good understanding and experience with infrastructure-as-code tool, we use Terraform to build and configure our Azure infrastructure.

• Track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment

• Experience managing both technical and non-technical stakeholders and resolving complex business and technical issues.

Why you'll love CUBE?

• Immediate global impact: CUBE is a well-established player in RegTech (we were around before RegTech was even a thing!), and our category-defining product is used by leading financial institutions around the world (including Revolut, Citi, and HSBC). We have an audience across 150 countries, and they love CUBE.
• Quantity & quality of data: The stage has literally been set: over the past 10 years, the five engineering teams at CUBE have built solid foundations for data collection, transformation, and classification. 
• A rich & complex dataset: The main dataset is not only already structured, but also longitudinal and multilingual. We've tracked changes to regulation over time and built in-house translation models for 60+ languages.
• Cutting-edge Regulation Transformation Engine: RegTransform is the technological magic that transforms regulatory data into regulatory intelligence at a scale and quality not possible at a human level. To enable this transformation, our data science team is actively  developing and deploying state-of-the-art NLP and ML models to extract key information (entities, relationships, ...) from the unstructured data and generate high quality structured data.  
• Always learning: Part of your job is to stay up-to-date with the latest research, and share your learning with the AI teams at CUBE. You'll have a training budget and a conference budget. In the mid-long term, we're aiming to collaborate with universities.
• Employee-first work-life policy: CUBE went fully remote before the pandemic even hit, because we wanted to define the future of work. As a CUBER, you'll be able to design your home office and choose your own work equipment.  Unable to work from home one week, or desperate for in-person interaction with colleagues? No problem—book a room in a coworking space.
• Sustainable, customer-driven growth: We are a bootstrapped company funded by customers and strategic private investment. This means that growth is sustainable, and product development is very closely aligned with customer needs.
• Extremely bespoke hiring process: At CUBE, we're trying to flip hiring on its head: the objective of the process is to create a personalized job description (and title). This page sets the general context. We'll collaboratively determine the best role for you, given your interests, CUBE's needs, and other members of the team.

More About CUBE

CUBE was founded in 2011 to transform the way global financial institutions manage regulatory change. Few financial institutions have instant access to the regulatory intelligence and analytics required to understand the impact of regulatory change, and tackle it, quickly and cost-effectively.

Utilizing Artificial Intelligence, Machine Learning and Natural Language Processing, CUBE’s enterprise-wide RegTech solution de-risks the regulatory change process and dramatically cuts compliance costs. CUBE is a fast-growing business, with offices in the UK, USA, and Australia. We serve multi-jurisdictional Tier 1/2 financial institutions, including global banks, wealth managers and insurance companies. 1.5-million staff in 180 countries consume regulatory intelligence, and manage regulatory change initiatives, powered by CUBE.

⏱️ Hiring timeline

We know how insufferably long and complicated hiring processes can be. We've been there before.

That's why at CUBE, we aim to compress the hiring timeline to between 5 and 10 days (from the first-round interview to the final round). There's no HR screen, culture fit interview, or coding on a whiteboard. Just high-quality info flow in both directions. 

Here's what will happen:

• Online application (link below)
• First round video interview with DevOps and Data Science Leads (45-60m)
• Take-home challenge
• Final round panel interview (45-60m)

If you have any questions at this stage, feel free to use the live chat widget on this page. Otherwise: what are you waiting for? This is your once-in-a-lifetime opportunity to define the future of regulation. The clock is already ticking!