Senior Applied AI Engineer (Computer Vision)
Core
Design and deploy production-grade computer vision systems for perception, understanding, and reasoning over visual data, integrating them into AI platforms and agent-based workflows.
Role type
Senior Applied AI Engineer (Computer Vision)
Builds
End-to-end visual intelligence systems, multimodal pipelines, and agent-based decision-making workflows
Domain
Computer Vision, Deep Learning, Multimodal AI
Deliverable
production ML models
Required skills
Deep learning frameworks (PyTorch, TensorFlow), Object detection, Segmentation, Multi-object tracking, Vision Transformers (ViT, Swin), CNNs (ResNet, EfficientNet), Geometric methods (Camera calibration, Epipolar geometry, Pose estimation), 3D reconstruction, Low-latency inference optimization, Data pipeline design, Dataset curation, Synthetic data generation
Preferred skills
Multimodal systems (CLIP, VLMs), 3D vision (NeRFs, SLAM, Point clouds), Video understanding (Action recognition), Active learning, Hard negative mining, Large-scale distributed training
Technologies
PyTorch, TensorFlow, YOLO, DETR, Mask R-CNN, CLIP, SORT, DeepSORT, ByteTrack, RNNs, Transformers
Responsibilities
Design and deploy computer vision systems for object detection, segmentation, tracking, scene understanding, and video understanding; Build and optimize models using CNNs, Vision Transformers, and detection/segmentation architectures; Develop multimodal systems combining vision and language; Implement algorithms for multi-object tracking, feature matching, and temporal modeling; Apply geometric and classical computer vision methods for camera calibration, pose estimation, and 3D reconstruction; Optimize systems for low-latency, real-time inference and edge deployment; Design and build data pipelines for annotation, curation, and synthetic data generation; Integrate vision systems into multimodal AI pipelines and agent-based workflows
Seniority
Senior, hands-on IC
