Computer Vision Engineer

Kula Example; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Kula Example in San Francisco seeks a Computer Vision Engineer to develop image and video analysis systems for our product suite. You will implement real-time pipelines and advanced ML models to power features across our platform.

Candidates should have 3+ years in CV and DL, strong Python and C++, and hands-on experience with OpenCV and ML frameworks. This role offers opportunities to optimize models for latency and integrate them into production.

Qualifications

• 3+ years of experience in computer vision and deep learning.
• Strong proficiency in Python and C++.
• Experience with OpenCV, TensorFlow, or PyTorch.
• Bachelor’s or Master’s in Computer Science or Electrical Engineering.

Responsibilities

• Design and deploy real-time computer vision pipelines to process and analyze visual data.
• Develop and train CNNs and vision transformer models for object detection, segmentation and classification.
• Collaborate with the engineering team to integrate models into production applications.
• Optimize models to improve computational efficiency and reduce latency.

Skills

Computer vision

Deep learning

Python

C++

Education

Bachelor's or Master's in Computer Science or Electrical Engineering

Tools

OpenCV

TensorFlow

PyTorch

Job description

About: Seeking a Computer Vision Engineer to develop image and video analysis systems for our product suite.

Requirements: 3+ years of experience in computer vision and deep learning, with a strong proficiency in Python and C++ programming languages, and expertise in OpenCV, TensorFlow, or PyTorch. Additionally, a Bachelor’s or Master’s degree in Computer Science or Electrical Engineering is required.

Responsibilities: Design and deploy real-time computer vision pipelines to efficiently process and analyze visual data, utilizing a combination of computer vision and machine learning algorithms. This involves developing and training convolutional neural networks (CNNs) and vision transformer models to achieve high accuracy in object detection, segmentation, and image classification tasks. Collaborate closely with the engineering team to integrate computer vision models into production applications, ensuring seamless integration and optimal performance. Furthermore, optimize CNN and vision transformer models to improve computational efficiency and reduce latency, thereby enhancing the overall user experience.