Computer Vision Engineer

Design, implement, and optimize deep learning models for a variety of computer vision tasks

Lead experiments and evaluations in domains such as security screening, biometrics, and remote sensing

Collaborate with cross-functional teams on data acquisition, annotation, and model deployment pipelines

Conduct rigorous model benchmarking and contribute to publications or patents as applicable

Stay up to date with the latest research in computer vision and deep learning; identify opportunities to integrate state-of-the-art methods

PhD in Computer Science, Electrical Engineering, or a related field, with a focus on computer vision, deep learning, or pattern recognition

4-5 years of post-PhD experience developing and deploying deep learning models for computer vision tasks

Strong expertise in frameworks such as PyTorch or TensorFlow

Proven experience with neural network architectures for detection, classification, and segmentation

Solid understanding of image processing, classical computer vision techniques, and modern deep learning methods

Excellent programming skills in Python; experience with C++ is a plus

Demonstrated ability to work independently and collaboratively in fast-paced, multidisciplinary teams

Strong communication and documentation skills

Experience with oriented bounding boxes, object detection in challenging domains (e.g., X-ray, satellite, or low-light imaging).

Background in face recognition algorithms, including familiarity with loss functions like ArcFace, triplet loss, or contrastive loss.

Publications or patents in top-tier conferences/journals related to computer vision.