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.

Key deliverables:

• Fine-tune open source LLM models for various applications, such as chatbots, question-answering systems, and recommendation engines.

• Pre-process and clean large text / instructions-based datasets for LLM finetuning.

• Able to tweak different prompts depending on the use case.

• Experiment with different LLM architectures and fine-tune models for specific tasks.

• Evaluate LLM performance and identify areas for improvement.

Responsibilities:

• Fine-tune open source LLM models for various applications, such as chatbots, question-answering systems, and recommendation engines.

• Pre-process and clean large text / instructions-based datasets for LLM finetuning.

• Able to tweak different prompts depending on the use case.

• Experiment with different LLM architectures and fine-tune models for specific tasks.

• Evaluate LLM performance and identify areas for improvement.

• Collaborate with backend team and DevOps to integrate LLMs into existing product stack.

• Stay up to date on the latest advancements in LLM research.

• May involve some contribution to defining product requirements and developing use cases for LLM models.

• Able to create LLM inference pipeline to productionize LLM and LM for multi-tenant SaaS Application

• Pre-train and fine-tune Language models for domain specific STS Task

Qualifications:

• Strong understanding of Natural Language Processing (NLP) techniques.

• Need 4-6 years of experience working as a Data Scientist.

• Proven experience in working with large text datasets.

• Hands on Experience on Prompt Engineering

• Excellent communication and collaboration skills.

• Experience with deep learning frameworks like TensorFlow or PyTorch.

• Experience with cloud platforms (AWS, GCP, Azure) a plus.

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.