Computer Vision Engineer

We are seeking a hands-on Computer Vision Engineer to build, train, and deploy visual intelligence solutions for real-world applications. The ideal candidate has strong experience developing computer vision models, working with image and video data, and optimizing AI workloads for cloud, edge, or robotics environments.Preferred location: Tampa, Florida.

Responsibilities

Train, fine-tune, evaluate, and deploy computer vision and multimodal AI models. Build solutions for object detection, classification, segmentation, tracking, pose estimation, OCR, and video analytics. Work with model families beyond YOLO, including CNN-based models, Faster R-CNN, Mask R-CNN, DETR variants, RT-DETR, Grounding DINO, SAM, CLIP, and vision-language models. Prepare and improve image/video datasets, annotations, augmentation pipelines, and model evaluation workflows. Optimize models for cloud, on-premises, and real-time edge deployment. Collaborate with engineering, product, robotics, and customer teams on prototypes, pilots, and production solutions.Required Qualifications:3+ years of experience in computer vision, machine learning, deep learning, or applied AI. Strong Python programming skills and practical experience with PyTorch or similar deep-learning frameworks. Demonstrated experience training and fine-tuning visual AI models, not only integrating pre-trained models or APIs. Experience with image/video data preparation, annotation, augmentation, model validation, and performance evaluation. Familiarity with object detection, segmentation, tracking, classification, and video analytics. Experience with Linux, Git, Docker, GPU-enabled development, and production-oriented model deployment. Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

Experience with ROS or ROS 2 and robotics perception pipelines. Programming experience in C++ for robotics, performance-sensitive applications, or embedded systems. Familiarity with robotics sensors and perception inputs, including RGB/depth cameras, stereo cameras, LiDAR, IMUs, GPS, or thermal cameras. Experience with edge AI and embedded platforms, especially NVIDIA Jetson devices such as Jetson Orin, Jetson AGX Orin, Jetson Xavier, and NVIDIA Thor. Experience with NVIDIA tools such as CUDA, TensorRT, DeepStream, Triton Inference Server, and ONNX Runtime. Familiarity with robotics or simulation environments such as NVIDIA Isaac Sim, Omniverse, Gazebo, or Webots. Experience with cloud-based training, inference, MLOps, synthetic data, or digital-twin workflows.What You'll Deliver:Accurate, efficient vision models trained for practical operating conditions. End-to-end visual intelligence pipelines, from dataset preparation and training through optimized deployment. Reusable capabilities for edge AI, robotics perception, and video analytics.