As a Senior AI Engineer with a focus on computer vision, you will be a key contributor to our research and development efforts. Your primary responsibility will be to lead the design, development, and deployment of advanced computer vision algorithms and models. You will work closely with cross-functional teams to deliver industry-leading AI solutions that make a significant impact on our products and services. This role offers an exciting opportunity to showcase your expertise in computer vision and contribute to the advancement of AI technologies.

Responsibilities:

Computer Vision Algorithm Development: Lead the research, design, and implementation of state-of-the-art computer vision algorithms, including but not limited to image recognition, object detection, image segmentation, and pose estimation.

Model Training and Optimization: Utilize your deep learning expertise to train, fine-tune, and optimize computer vision models using popular deep learning frameworks such as TensorFlow or PyTorch.

Data Preprocessing and Augmentation: Work with large-scale datasets, applying data preprocessing and augmentation techniques to ensure high-quality inputs for computer vision tasks.

Performance Evaluation: Evaluate and benchmark the performance of computer vision models, utilizing appropriate metrics and methodologies to ensure accuracy and efficiency.

Research and Innovation: Stay up-to-date with the latest research papers and advancements in computer vision. Apply cutting-edge techniques to solve complex AI challenges.

Cross-Functional Collaboration: Collaborate with software engineers, data scientists, and product managers to integrate computer vision capabilities into our products and deliver end-to-end AI solutions.

Prototyping and Proof of Concept: Rapidly create prototypes to demonstrate the feasibility and potential impact of computer vision solutions in real-world scenarios.

Technical Leadership: Mentor and support junior AI engineers, providing technical guidance and expertise to facilitate their professional growth.

Documentation: Maintain comprehensive documentation of algorithms, models, and development processes to support knowledge sharing and future research.