AI and Synthetic Data Engineer

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: SkillsStrong 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.