Top Tech Jobs & Startup Jobs

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.

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

Own applied computer vision and machine learning development, translating business problems into scalable solutions. Build, fine-tune, evaluate, and deploy models for object detection, segmentation, classification, video analysis, forecasting, anomaly detection, regression, and clustering. Apply knowledge of cameras, sensors, lighting, and edge-device constraints to ensure robust production performance.

Develop and deploy production computer vision and machine learning solutions for object detection, segmentation, classification, video analysis, forecasting, and anomaly detection. Responsibilities include selecting and fine-tuning models with PyTorch or TensorFlow, analyzing performance, translating business problems into scalable AI solutions, and accounting for real-world constraints involving cameras, sensors, lighting, and edge-device compute limitations.

Supports large-scale AI workflows involving image and video dataset curation, annotation management, model training, evaluation, benchmarking, documentation, and failure-case analysis. The role collaborates with senior engineers to improve data and algorithms while developing foundational experience in applied modeling or MLOps.

Build and operate MLOps infrastructure for computer vision models, moving them from experimentation into reliable cloud and edge production. Responsibilities include CI/CD pipelines, dataset and model versioning, monitoring, drift detection, GPU optimization, experiment tracking, and scalable deployment using Docker and Kubernetes. Collaborate with data scientists and AI researchers to optimize latency, throughput, batching, GPU memory usage, and system costs.