D. in Computer Science, Engineering, or a related technical field, or equivalent practical experience Hands-on experience building and improving computer vision or perception models in production or real-world systems, especially for object detection, object tracking Strong understanding of model evaluation, including selecting metrics, building test sets, analyzing false positives/false negatives, and measuring performance over time for iterative improvement. Design and improve training pipelines to support faster, continuous model iteration Enable scalable experimentation across datasets, model architectures, and training strategies Improve reproducibility, experiment tracking, and comparison of training runs.