Position: Machine Learning Engineer
Location: Remote – The Hague – The Netherlands – EMEA

Are you an ambitious Machine Learning Engineer and want to be at the forefront of Generative AI and solve the issue of data privacy to accelerate data innovation? Then BlueGen.ai has a fantastic opportunity for you.

At a glance.

BlueGen.ai is a synthetic data platform at the forefront of generative AI and the next generation of privacy-enhancing technology. Its technology sits at the crossroads of cutting-edge machine learning in the form of Generative Adversarial Networks and Diffusion Models, together with Differential Privacy and distributed systems. As a Machine Learning Engineer, you can make your mark to take BlueGen.ai’s product to the next level. Besides strong analytical, machine learning, and coding skills, it would be great if you were an assertive professional with an entrepreneurial spirit.

Your role.

Your role involves translating new research into a product that solves real-world problems. Specific activities range from implementing new models, designing data encoding schemes, and running experiments; to deploying optimised inference pipelines and packaging everything in a working project. You will collaborate with our software engineering team and research lab to build new features and systems.

Your profile.

What we offer you.

Interested?

For more information about the role, please get in touch with Iman Alipour at [email protected] or +31(0)850602560.

About BlueGen.ai.

BlueGen.ai – a spin-off of the Delft University of Technology – is a robust privacy-preserving data synthesizing platform that supersedes existing methods, such as data anonymization, both in terms of privacy and data usability. It generates data that mimics real datasets reliably using the power of AI. BlueGen features a novel distributed framework that ensures the client’s data stays safe on-premises. Moreover, it incorporates differential privacy to bolster privacy with mathematical guarantees. BlueGen can potentially improve the development of digital tools and skills for tackling problems collectively by creating an efficient data-driven ecosystem that safely fosters sharing insights across industries and disciplines. In addition, it may be used to re-distribute valuable datasets possessed by big corporations to the general public, allowing equal opportunities in society to work with datasets.