10 ott - Italia
Hermes Corporate
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
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