Are you passionate about teaching machines to see, interpret, and understand the visual world? Do you thrive on applying computer vision techniques to solve complex real-world problems, from object detection to image segmentation and facial recognition? If you’re excited about pushing the boundaries of what’s possible with visual intelligence, then our client has an amazing opportunity for you. We’re looking for a Computer Vision Engineer (aka The Visual Intelligence Architect) to design, develop, and deploy computer vision solutions that will transform industries and elevate product experiences.

As a Computer Vision Engineer at our client, you’ll work with large datasets, develop cutting-edge computer vision algorithms, and implement solutions that have a direct impact on applications in fields like autonomous vehicles, healthcare, retail, and security. You’ll work alongside a talented team of engineers and data scientists to bring visual intelligence to life in innovative products.

Key Responsibilities:

1. Develop and Optimize Computer Vision Algorithms:
• Design and implement computer vision algorithms using state-of-the-art techniques such as convolutional neural networks (CNNs), image segmentation, object detection, and facial recognition. You’ll develop models using frameworks like TensorFlow, PyTorch, or OpenCV.
2. Image and Video Processing:
• Preprocess and analyze image and video data to develop models that can accurately detect and classify objects, track motion, and recognize patterns. You’ll apply techniques like edge detection, feature extraction, and background subtraction to create robust systems.
3. Train and Fine-Tune Vision Models:
• Train deep learning models for tasks like object detection, image classification, and video analysis. You’ll experiment with different model architectures, optimize hyperparameters, and fine-tune models for real-world deployment.
4. Deploy and Scale Vision Solutions:
• Work with cross-functional teams to deploy computer vision models into production environments. You’ll ensure that models are scalable, efficient, and integrated with real-time systems, whether deployed in the cloud or on edge devices.
5. Model Evaluation and Performance Monitoring:
• Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score. You’ll monitor models in production, retraining and optimizing them as necessary to maintain high levels of accuracy and performance.
6. Collaborate with Cross-Functional Teams:
• Partner with data scientists, software engineers, and product managers to understand business requirements and develop solutions that align with product goals. You’ll ensure that your computer vision models deliver real-world value and solve business-critical problems.
7. Stay Updated on Industry Trends:
• Keep up with the latest research and advancements in computer vision and deep learning. You’ll experiment with cutting-edge techniques such as generative adversarial networks (GANs), transformers, and self-supervised learning, integrating them into the company's solutions when applicable.

Requirements

Required Skills:

• Computer Vision Expertise: Strong knowledge of computer vision techniques, including object detection, image segmentation, optical flow, 3D reconstruction, and facial recognition. You’re experienced with state-of-the-art deep learning models like CNNs, ResNet, YOLO, and EfficientNet.
• Programming and Tools: Proficiency in Python and experience with computer vision frameworks and libraries like OpenCV, TensorFlow, PyTorch, or Keras. You can implement custom vision algorithms and optimize them for performance and accuracy.
• Data Processing and Feature Engineering: Expertise in processing and augmenting large datasets for computer vision tasks. You understand image preprocessing techniques like scaling, normalization, and data augmentation to improve model performance.
• Deployment Experience: Experience deploying computer vision models in production environments using cloud platforms (AWS, GCP, Azure) or on edge devices. Familiarity with tools like Docker, Kubernetes, and TensorFlow Serving is a plus.
• Research and Innovation: Interest in exploring and applying new research in computer vision and deep learning. You can identify opportunities to improve existing models and experiment with new architectures and techniques.

Educational Requirements:

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science, or a related field. Equivalent experience in computer vision engineering is also highly valued.
• Certifications or additional coursework in computer vision, deep learning, or AI are a plus.

Experience Requirements:

• 3+ years of experience in computer vision engineering, with a proven track record of developing and deploying computer vision models in real-world applications.
• Experience working with large-scale image and video datasets and implementing deep learning models to solve complex visual challenges.
• Hands-on experience with cloud-based services for deploying and scaling computer vision models is highly desirable.

Benefits

• Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
• Work-Life Balance: Flexible work schedules and telecommuting options.
• Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
• Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
• Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
• Tuition Reimbursement: Financial assistance for continuing education and professional development.
• Community Engagement: Opportunities to participate in community service and volunteer activities.
• Recognition Programs: Employee recognition programs to celebrate achievements and milestones.