Principal Computer Vision Scientist

Senior technical leader for our Computer Vision and AI/ML work. You'll own model architecture and experimentation through to production, and set the standards the team builds against. PhD-level role spanning research, engineering, and production systems. What you'll do Lead research, design, and delivery of CV and AI/ML solutions Define model architecture, experiment strategy, validation methodology, and production-readiness criteria Train, fine-tune, and deploy models for object detection, segmentation, classification, OCR, feature matching, and visual search Own the full model lifecycle: dataset quality, annotation, training, evaluation, deployment, monitoring Build prototypes and PoCs; analyze failure cases and drive improvements Review and improve CV pipelines for latency, scalability, and reliability Set standards for evaluation, versioning, dataset management, and MLOps; mentor engineers What you'll need PhD in CS, Computer Vision, ML, AI, Applied Math, EE, Robotics, or related field 7+ years hands-on ML/deep learning with a strong CV focus Strong Python; PyTorch and/or TensorFlow; OpenCV, NumPy, Pandas, scikit-learn Solid grasp of classical CV and modern architectures (CNNs, ViT, YOLO, Mask R-CNN, CLIP/SAM-like) Proven experience taking models to production and optimizing for latency, memory, and scale REST APIs, Docker, CI/CD, model versioning, experiment tracking, MLOps Strong data/annotation/error-analysis discipline; Agile; ownership mindset Nice to have Edge/mobile inference (ONNX, TensorRT, OpenVINO, TFLite, CoreML) · large-scale image pipelines · synthetic data, active learning, weak supervision · multimodal/vision-language models · cloud ML platforms · publications or patents