Computer Vision Engineer

Company Description Chooch helps build and run the best Computer Vision AI applications that make cameras intelligent, enhancing productivity & revenues, while reducing operational costs. Chooch has been named a leading computer vision platform by IDC. Role Description We are looking for a Computer Vision/ Machine Learning Engineer (US-based) to join our team. This is a full-time, remote role with frequent hands-on experience with customer projects in the Bay Area. The Machine Learning Engineer will be responsible for the successful design, accuracy, and backbone behind our Computer Vision AI applications. This includes data preparation, training, configuring neural networks, deploying networks in real customer locations, and refining computer vision algorithms for healthcare, manufacturing, and retail customers. This role requires frequent interaction with technical and non-technical customers while working closely within our solutions engineering teams. The Machine Learning Engineering role has opportunity to grow, lead technical projects, and improve current processes while providing meaningful feedback to our product and solutions teams. Qualifications Proficiency in machine learning frameworks (e.g., PyTorch) Experience with Large-language models Strong programming skills in Python Experience with image processing and computer vision libraries (e.g., OpenCV). Familiarity with cloud computing services (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). Experience with software development, machine learning in practice, computer vision, and ML in production Excellent problem-solving and troubleshooting skills Strong communication and interpersonal skills Ability to work independently and as part of a team Comfortability on the command-line Experience with version control systems such as Git Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field with a focus on machine learning or computer vision. Key Responsibilities Model Development and Optimization: Design, develop, and optimize machine learning models for computer vision applications. Data Management: Collaborate with ML engineers to ensure the collection, processing, and maintenance of high-quality datasets necessary for model training and testing. Algorithm Implementation: Implement state-of-the-art computer vision algorithms to improve system accuracy and performance in real-world scenarios. Cross-Functional Collaboration: Work closely within the solutions engineering team to integrate machine learning models into the broader product and systems. On-Site Travel and Field Testing: Many of our projects are live deployments with major fortune 500 customers – testing new, innovative ML technologies. Traveling on site is sometimes needed to see the environment, test in the real-world, and ensure the technology is operating as expected. Performance Evaluation: Regularly evaluate the performance of computer vision models, using both qualitative and quantitative methods, and iterate to enhance accuracy and efficiency. Research and Innovation: Understanding of the latest developments in machine learning and computer vision, applying innovative approaches and technologies to solve complex challenges. Stakeholder Engagement: Collaborate with internal and external stakeholders to understand their needs and translate them into effective technical solutions. Problem-Solving: Address and troubleshoot complex problems that arise during the development and deployment of computer vision systems. The ideal candidate should be deeply passionate about machine learning and its real-world applications, particularly in improving and leveraging state-of-the-art neural networks to make an impact.