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Supports AI teams by curating image and video datasets, managing annotation workflows, running model training and evaluation jobs, maintaining benchmarks, documenting results, and analyzing model failure cases. The role requires foundational Python, machine learning, computer vision, and cloud knowledge, with opportunities to grow into applied modeling or MLOps.

Supports AI/ML workflows involving large-scale image and video datasets. Responsibilities include curating datasets, managing annotation processes, running model training and evaluation jobs, maintaining benchmarks, documenting results, tracking model performance, and analyzing failure cases with senior engineers. The role requires foundational Python, machine learning, computer vision, cloud, and data-quality skills, with opportunities to grow into applied modeling or MLOps.

Supports AI/ML workflows involving large-scale image and video datasets. Responsibilities include curating datasets, managing annotation workflows, running model training and evaluation jobs, maintaining benchmarks, documenting results, tracking model performance, and analyzing failure cases with senior engineers. The role provides foundational experience toward applied modeling or MLOps.

Develop and deploy computer vision and machine learning solutions for object detection, segmentation, classification, video analysis, forecasting, and anomaly detection. Evaluate and fine-tune PyTorch and TensorFlow models, analyze performance, and address real-world constraints involving lighting, sensors, cameras, and edge-device computing. Translate business problems into scalable production AI solutions while ensuring data quality and robust inference.

Supports AI and computer vision workflows involving large-scale image and video dataset curation, annotation management, model training, evaluation, benchmarking, and failure-case analysis. The engineer documents results, monitors model performance, and collaborates with senior engineers to improve data and algorithms. The role requires foundational Python, machine learning, computer vision, and cloud knowledge, with opportunities to grow into applied modeling or MLOps.

Develop and deploy computer vision and machine learning solutions for object detection, segmentation, classification, video analysis, forecasting, and anomaly detection. Translate business problems into scalable AI/ML systems, evaluate and fine-tune PyTorch or TensorFlow models, analyze performance, and account for real-world constraints involving lighting, sensors, cameras, and edge-device computing.

Develop and deploy production computer vision and machine learning solutions for object detection, segmentation, classification, video analysis, forecasting, and anomaly detection. Evaluate and fine-tune PyTorch and TensorFlow models, analyze performance, and account for real-world constraints involving cameras, sensors, lighting, and edge-device computing limitations.

Own applied computer vision and machine learning development, including object detection, segmentation, classification, video analysis, forecasting, anomaly detection, and other data science models. Train, fine-tune, evaluate, and deploy production models using PyTorch and TensorFlow. Address real-world constraints involving cameras, sensors, lighting, data quality, and edge-device performance. The role is onsite in Dallas, Texas, for US work-authorized candidates.

Own applied computer vision and machine learning development from problem definition through production deployment. Build and fine-tune models for object detection, segmentation, classification, video analysis, forecasting, anomaly detection, regression, and clustering. Evaluate model performance and account for real-world constraints involving lighting, sensors, cameras, and edge-device computing. The role is onsite in Dallas, Texas for US-based engineers and requires strong Python, PyTorch or TensorFlow, and production ML experience.

Develop and deploy production computer vision and machine learning solutions for object detection, segmentation, classification, video analysis, forecasting, anomaly detection, regression, and clustering. Evaluate and fine-tune PyTorch and TensorFlow models while accounting for real-world constraints involving cameras, sensors, lighting, data quality, and edge-device compute limitations.