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Synthetic Data Engineer

Job in 2490, Den Haag, Netherlands
Listing for: Qabird
Full Time position
Listed on 2026-04-21
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Engineer, Data Scientist
  • Engineering
    AI Engineer (Applied/Software), Data Engineer
Salary/Wage Range or Industry Benchmark: 60000 - 80000 EUR Yearly EUR 60000.00 80000.00 YEAR
Job Description & How to Apply Below

Overview

Position:
Synthetic Data Engineer

Location:

The Hague – The Netherlands // Remote

Are you an ambitious and talented engineer and do you want to be at the forefront of Generative AI and solve the issue of data privacy to accelerate data innovation? Then Blue Gen.ai has a fantastic opportunity for you.

Interested?

For more information about the role, please contact us at careers and we will get in touch with you.

Your at-a-glance

Blue Gen.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, such as Diffusion Models and Flow Matching, together with Differential Privacy and distributed systems. As a Synthetic Data Engineer, you can make your mark to take Blue Gen.ai’s product to the next level also working with Enterprise European customers.

Besides analytical, machine learning, and coding skills, we are looking for an assertive professional with an entrepreneurial spirit.

Your role

Your role centers around translating state of the art research into a product that solves real-world problems. This involves working with customers to help them generate high quality privacy safe synthetic data, implementing algorithms for handling different data types, designing robust software that can deal with the inherent complexities of real-life data, and analyzing failure cases to improve the quality of our synthetic data.

You will be part of our software engineering team and build new features and systems with cutting edge technology. You will have the opportunity to work on individual development goals, and grow into senior roles.

Your profile
  • You have computer science and/or artificial intelligence knowledge at the MSc level
  • You have experience in machine learning and general programming, specifically:
  • Required:

    Python
  • Preferred:
    Pandas, Num Py, ETL pipelines, Matplotlib, RDBMS
  • Bonus:
    PyTorch, GPU acceleration, Tensorboard, Time series forecasting
  • You are self-sufficient and an individual contributor
  • You can adapt to our product vision and technology quickly
  • You can bring innovation to the team and the product from a technical perspective
  • You feel comfortable engaging with customers
  • You have a start-up and can-do mentality
  • You are pragmatic and flexible, and a communicator
What we offer you
  • Working in a team with top level engineers and business professionals with proven track records
  • Working in a varied environment: balancing between the market requirements and science
  • Working with our innovative enterprise customers
  • Be part of bringing state-of-the-art technology to an emerging market
  • Focus on your personal development, both professionally and individually
  • A great culture where everyone is equal and helps each other
  • Flexible working arrangements to organize your perfect work-life balance
  • A competitive salary package where stock options are negotiable
About Blue Gen.ai

Blue Gen.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. Blue Gen 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. Blue Gen 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.

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