Senior Computer Vision Engineer

About the role

We are looking for a Senior Computer Vision Engineer to own applied model development across computer vision, machine learning, and data science use cases — building and deploying solutions for object detection, image segmentation, classification, and video analysis. You will evaluate and fine-tune model architectures using PyTorch and TensorFlow, build broader ML models for forecasting and anomaly detection, and apply practical knowledge of computer vision hardware including cameras, sensors, and edge devices.

What you will do

• Own the applied model development process across computer vision, AI/ML, and broader data science use cases;
• Translate complex business problems into viable, practical, and scalable AI/ML solutions;
• Evaluate various model options, train and fine-tune selected architectures, and rigorously analyze model performance;
• Develop and deploy solutions for object detection, image segmentation, image classification, and video analysis;
• Build and maintain models for time-series forecasting, anomaly detection, regression, clustering, and general data analysis;
• Apply practical knowledge of real-world constraints—such as lighting, sensor limitations, and edge device compute power—to ensure optimal data quality and robust model performance in production.

Must haves

• 3 to 5 years of professional experience in Computer Vision, Machine Learning, Data Science, or a related field;
• Degree in Computer Science, Engineering, Data Science, Mathematics, or a related discipline (or equivalent practical experience);
• Engineers located in the US must reside in Dallas, TX, and be open to working from the office (onsite);
• Strong, production-level proficiency in Python;
• Deep hands-on experience with PyTorch and/or TensorFlow;
• Proven track record of building and deploying models for detection, segmentation, classification, and image/video analysis;
• Solid understanding of broader ML and data science techniques (time-series modeling, forecasting, anomaly detection, regression, and clustering);
• Practical experience working with computer vision hardware, including cameras, sensors, and lighting setups;
• Familiarity with deploying models on edge devices;
• Strong understanding of how physical and real-world constraints impact data quality, model training, and inference;
• Upper-intermediate English level.