Introduction
We are seeking a Synthetic Data Engineer who specializes in generating artificial datasets for training and validating machine learning models. If you are passionate about privacy-preserving data generation, bias mitigation, and advancing data-centric AI practices, this role offers a unique opportunity to strengthen AI systems at their core.
As a Synthetic Data Engineer at our organization, you will design pipelines that produce realistic, diverse, and high-quality synthetic datasets. You’ll work closely with data scientists, ML engineers, and compliance teams to ensure synthetic data meets accuracy, fairness, and privacy requirements.
We offer competitive compensation, comprehensive benefits, and opportunities to innovate in one of the fastest-growing areas of AI.
Objectives of this role
- Build scalable synthetic data generation pipelines for AI/ML training.
- Ensure synthetic datasets maintain statistical validity and realism.
- Address privacy, fairness, and bias issues through controlled data generation.
- Collaborate with AI teams to validate synthetic data against real-world benchmarks.
Your tasks
- Develop generative models (GANs, VAEs, diffusion models) for synthetic data.
- Implement workflows to balance data diversity, privacy, and utility.
- Test and validate synthetic datasets across ML tasks and domains.
- Automate synthetic data pipelines for reproducibility and scalability.
- Ensure compliance with privacy regulations in data generation processes.
- Publish research and best practices in synthetic data engineering.
Required skills and qualifications
- Bachelor’s degree in Computer Science, Data Science, or related field.
- Strong programming skills in Python and ML frameworks (TensorFlow, PyTorch).
- Experience with generative modeling and simulation techniques.
- Knowledge of privacy-preserving data methodologies.
- Familiarity with ML pipelines and data preprocessing.
Preferred skills and qualifications
- Advanced degree in AI, Data Engineering, or Computational Modeling.
- Experience with synthetic data frameworks (Synthia, Unity, Gretel).
- Contributions to generative AI research or open-source tools.
- Familiarity with fairness and bias testing frameworks.
- Knowledge of regulated industries such as healthcare or finance.
