Senior Synthetic Data Engineer – Generative & Diffusion Models

Opportunity Overview

Location: 100% Remote (United States)
Employment Type: Full-Time Direct-Hire via Accel DNA
Compensation Range: $175,000 – $210,000 Base + Bonus
Hiring Agency: Accel DNA – Premier Frontier Data & AI Talent Partner

Develop generative synthetic data pipelines to train and benchmark models where real-world training data is scarce, expensive, or privacy-restricted.

Key Responsibilities:

• Design synthetic tabular, text, and multimodal data generation pipelines using diffusion models, GANs, and LLM prompting.
• Implement statistical fidelity verification (Wasserstein distance, correlation preservation) comparing synthetic and empirical distributions.
• Ensure complete differential privacy guarantees and zero membership inference vulnerability in generated datasets.
• Collaborate with ML modeling squads to demonstrate model performance parity trained on synthetic data.

What You Bring:

• 5+ years in data science or machine learning with dedicated focus on generative modeling.
• Strong command of PyTorch, differential privacy frameworks (Opacus), and statistical distribution metrics.
• Experience generating complex relational synthetic databases with foreign key integrity.
• MS/PhD in Statistics, Machine Learning, or related quantitative discipline.

How to Apply:

Interested in exploring this opportunity? Submit your profile or portfolio directly to contact@accel-dna.com or apply through https://accel-dna.com/contact-us/.