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.