Computer Vision Engineer

We are looking for a skilled Computer Vision&Robotics Engineer to design, develop, and deploy advanced vision-based solutions for real-world applications. You will work at the intersection of AI and robotics, building robust systems that operate reliably in production and real-time environments.

Key Responsibilities

• Design, develop, and deploy computer vision models for real-world applications
• Build and optimize deep learning models for tasks such as object detection, segmentation, classification, tracking, and pose estimation
• Develop scalable image and video processing pipelines for both training and inference
• Deploy and optimize models for real-time and edge environments, ensuring low latency and high efficiency
• Integrate vision models into production systems, including automated and semi-autonomous platforms
• Collaborate with cross-functional teams (software, hardware, product) to deliver end-to-end solutions
• Evaluate model performance using real-world data and continuously improve accuracy, robustness, and efficiency
• Stay up to date with the latest advancements in computer vision, deep learning, and applied AI

Required Qualifications

• Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field
• 2+ years of hands-on experience in computer vision and deep learning
• Strong programming skills in Python
• Experience with deep learning frameworks such as PyTorch or TensorFlow
• Solid understanding of core computer vision concepts (image processing, CNNs, feature extraction, object detection)
• Experience training and deploying machine learning models in production environments
• Familiarity with model optimization techniques (e.g., quantization, pruning, ONNX)
• Experience working with image/video datasets and data pipelines
• Strong analytical and problem-solving skills

Preferred Qualifications

• Master’s degree in Computer Vision, Machine Learning, or a related field
• Experience with real-world or industrial AI applications
• Exposure to robotics or autonomous systems
• Familiarity with edge deployment or performance-constrained environments
• Experience with cloud-based ML infrastructure and MLOps workflows

Nice to Have

• Experience with infrastructure robustness testing to ensure system stability and reliability
• Hands-on experience updating and validating software on physical robotic systems
• Exposure to real-world field testing and evaluating system performance in live environments
• Familiarity with end-to-end system validation, including testing under varying operating conditions