AI and Synthetic Data Engineer

Veriipro; Irving (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Veriipro is seeking an experienced AI and Synthetic Data Engineer to develop synthetic-data pipelines and AI experiments for computer vision and Physical AI applications. You will work with simulated industrial environments and sensor data to advance perception systems.

You will collaborate with cross-functional teams to design, implement, and validate data-generation workflows, baselines, and reproducible research releases while improving data quality and model performance.

Qualifications

• Strong experience in AI/ML engineering, computer vision, and synthetic-data generation.
• Proficiency in Python and deep-learning frameworks such as PyTorch or TensorFlow.
• Understanding of object detection, image segmentation, ground-truth labeling, dataset bias, and model evaluation.
• Experience with dataset management, experiment reproducibility, and ML pipeline automation.

Responsibilities

• Develop synthetic-data pipelines to generate labeled images, videos, point clouds, depth maps, segmentation masks, pose annotations, and 3D scene data.
• Implement domain randomization, scenario coverage, class balancing, and dataset versioning strategies.
• Train, fine-tune, and evaluate perception models using synthetic, real-world, and blended datasets.
• Analyze the synthetic-to-real domain gap and improve model generalization through simulation, augmentation, and domain adaptation.
• Automate data generation, experiment tracking, metadata capture, quality checks, and reproducible dataset releases.
• Collaborate with AI, computer vision, and simulation teams to improve data quality and model performance.

Skills

AI/ML engineering

Computer vision

Synthetic-data generation

Python

Job description

We are looking for an experienced AI and Synthetic Data Engineer to develop synthetic-data pipelines and AI experiments for computer vision, perception, and Physical AI applications using simulated industrial environments and sensor data.

Key Responsibilities

• Develop synthetic-data pipelines to generate labeled images, videos, point clouds, depth maps, segmentation masks, pose annotations, and 3D scene data.


• Implement domain randomization, scenario coverage, class balancing, and dataset versioning strategies.


• Train, fine-tune, and evaluate perception models using synthetic, real-world, and blended datasets.


• Analyze the synthetic-to-real domain gap and improve model generalization through simulation, augmentation, and domain adaptation.


• Automate data generation, experiment tracking, metadata capture, quality checks, and reproducible dataset releases.


• Collaborate with AI, computer vision, and simulation teams to improve data quality and model performance.

Required Skills

• Strong experience in AI/ML engineering, computer vision, and synthetic-data generation.


• Proficiency in Python and deep-learning frameworks such as PyTorch or TensorFlow.


• Understanding of object detection, image segmentation, ground-truth labeling, dataset bias, and model evaluation.


• Experience with dataset management, experiment reproducibility, and ML pipeline automation.