Synthetic Data Engineer Jobs & Internships 2026
Synthetic data engineers build AI systems that generate artificial training data — photorealistic images of rare scenarios, synthetic tabular datasets that preserve statistical properties without exposing sensitive individual records, and simulated text datasets for low-resource language and domain applications. As real-world data collection becomes increasingly constrained by privacy regulations, cost, and the difficulty of capturing rare events at sufficient frequency, synthetic data has become a strategic capability for AI teams. The field spans generative AI, simulation engineering, and privacy-preserving data techniques.
What Does a Synthetic Data Engineer Do?
Synthetic data engineers build photorealistic 3D rendering pipelines that produce labeled training images of scenarios that are difficult or dangerous to capture in the real world — rare defects, hazardous environments, nighttime road conditions. They implement generative model pipelines using GANs, VAEs, and diffusion models that create new tabular or image data that statistically resembles real datasets while not exposing individual records. Quality evaluation is a critical challenge: measuring whether synthetic data is diverse enough to prevent model overfitting and faithful enough to the real data distribution to train models that generalize to real inputs. Privacy validation frameworks quantify how well synthetic data prevents re-identification of individuals from the original dataset. They work with domain experts to ensure synthetic scenarios cover the distribution of cases that deployed models will encounter.
Required Skills & Qualifications
- ✓Generative model design: GANs, VAEs, and diffusion models for tabular and image synthesis
- ✓3D simulation environments: NVIDIA Omniverse, Blender, or Unreal Engine for image synthesis
- ✓Synthetic data quality evaluation: fidelity, diversity, and model utility metrics
- ✓Privacy assessment: membership inference tests and re-identification risk analysis
- ✓Domain randomization for sim-to-real transfer in computer vision applications
- ✓Statistical data synthesis: CTGAN, TVAE, and copula-based tabular data generation
- ✓Ground truth annotation generation from simulation metadata
- ✓Data augmentation pipelines that blend real and synthetic data optimally
A Day in the Life of a Synthetic Data Engineer
Morning begins reviewing the output of a new synthetic face dataset generation pipeline — running a face recognition model on the synthetic data to verify it achieves comparable accuracy to the real-data baseline before releasing it for external model training. A bias in the synthetic population distribution (overrepresentation of one demographic group) is flagged and a resampling strategy is implemented to match the target distribution. Late morning involves fine-tuning a 3D simulation pipeline for automotive sensor fusion training — adjusting rain intensity parameters and headlight configurations to better match the real-world edge cases where the production model is failing. After lunch, a presentation to the enterprise customer team demonstrates the synthetic tabular data privacy evaluation results, showing that the synthetic dataset passes membership inference tests at the required privacy threshold. Afternoon is spent implementing a new diversity metric that measures the coverage of the synthetic dataset across the semantic space of the target domain.
Career Path & Salary Progression
ML Intern → Synthetic Data Engineer I → Senior Synthetic Data Engineer → Principal Synthetic Data Scientist → Head of Data
| Level | Base Salary | Total Comp (with equity) | Intern Monthly |
|---|---|---|---|
| Intern | — | — | $7,500–$11,500/mo |
| Entry-Level (0–2 yrs) | $105,000–$155,000 | +20–40% in equity/bonus | — |
| Mid-Level (3–5 yrs) | $155,000–$217,000 | +30–60% in equity/bonus | — |
| Senior (5–8 yrs) | $217,000–$303,000 | +50–100% in equity/bonus | — |
Salary data sourced from Levels.fyi, Glassdoor, and company disclosures. 2026 estimates.
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Synthetic Data Engineer — Frequently Asked Questions
Why is synthetic data increasingly important for AI training?
Several converging factors drive synthetic data adoption: GDPR and similar regulations restrict the use of personal data; real-world datasets often underrepresent rare but important scenarios (rare diseases, edge case road scenarios); labeling real data is expensive; and competitive sensitivity makes companies reluctant to share real data even internally. Synthetic data solves all these problems simultaneously when generated with sufficient quality.
What is the privacy guarantee of synthetic tabular data?
Unlike cryptographic privacy guarantees, synthetic tabular data offers probabilistic privacy assurances that depend on how well the generative model was trained. Membership inference attacks test whether an attacker can determine if a specific record was in the training dataset. Differential privacy noise added during synthetic data generation provides formal guarantees, but at the cost of reduced statistical fidelity.
What is Mostly AI and how do they generate synthetic tabular data?
Mostly AI uses deep learning (specifically transformer and GAN-based models) to learn the statistical distributions and correlations in tabular enterprise datasets, then generate arbitrarily large synthetic datasets that preserve these statistical properties. Their privacy guarantees are validated through membership inference testing. They primarily target regulated industries like finance and healthcare where real data sharing is restricted.
How does domain randomization make synthetic image data more useful?
Domain randomization varies properties like lighting, texture, camera angle, and background across synthetically generated training images. This wide variation in visual appearance forces the model to learn robust features that generalize across domains rather than memorizing domain-specific artifacts. When models trained on domain-randomized synthetic data are deployed on real-world images, the sim-to-real gap is smaller than when trained on photorealistic but less varied synthetic data.
What is Synthesis AI and how do they differ from Gretel?
Synthesis AI focuses specifically on generating synthetic human-centric image data for training facial recognition, emotion detection, and human pose estimation models — providing diverse, privacy-preserving synthetic people for training vision models. Gretel focuses on tabular and time series synthetic data for enterprise use cases in finance, healthcare, and IT operations. Both serve the synthetic data market but for different data types and applications.