Model Design & Development: Architect, train, and evaluate state-of-the-art computer vision models for tasks such as object detection, segmentation, pose estimation, scene understanding, and multimodal perception.
Algorithm Optimization: Optimize deep learning architectures for real-time inference, low-latency execution, and hardware efficiency across cloud and edge infrastructure. Pipeline Engineering: Build and maintain scalable data ingestion, annotation, synthesis, and continuous evaluation pipelines for large-scale visual datasets. Cross-Functional Collaboration: Partner closely with HCI designers, ML researchers, and software engineers to integrate computer vision capability into user-facing product features. Research & Innovation: Stay at the forefront of AI research. Education: Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Computer Vision, Electrical Engineering, or a related technical field. Hands-on Experience: 3+ years of professional or research experience developing and deploying production-grade computer vision models. Core Skills Deep Learning Expertise: Demonstrated understanding of modern neural network architectures (CNNs, Vision Transformers, attention mechanisms, feature extraction). Deployment & MLOps: Deployment & MLOps: Experience with model quantization, pruning, fine-tuning, and deployment runtimes (e.g., ... Experience working with multimodal models (integrating vision, text, audio, or sensor telemetry). Familiarity with Human-Computer Interaction (HCI) applications, eye-tracking, or spatial computing platforms. Background in synthetic data generation (using engines like Unreal, Unity, or Blender). Track record of peer-reviewed publications at top AI/CV conferences (CVPR, ICCV, ECCV, NeurIPS).
If multiple Education and Language Profiles are defined, please note that you must fit at least one of them, but not necessarily all.
| Subject | Level | Grade |
|---|---|---|
| COMPUTER SCIENCE | MASTERS | N.A. |
