Machine Learning Engineer (HROS)

Machine Learning Engineer

We are seeking a highly skilled Machine Learning Engineer to join our factory software machine learning and computer vision team. As a key member of the team, you will design, develop, and deploy production-grade ML models that power visual inspection, defect detection, and process monitoring across our factory and warehouse environments. You will take ambiguous operational challenges and build end-to-end solutions, leveraging cutting-edge techniques in computer vision, deep learning, and multi-modal data processing.

Responsibilities:

• Design, develop, and implement computer vision and machine learning models for factory and warehouse applications, including defect detection, visual inspection, process monitoring, and quality assurance using techniques like object detection, segmentation, and anomaly detection
• Build end-to-end ML pipelines from data collection and labeling through training, evaluation, and deployment across diverse factory environments
• Work with diverse, heterogeneous datasets combining multiple modalities including images, multi-spectral sensor outputs, video, text, and tabular data to build scalable solutions
• Take ownership of production models, ensuring robust monitoring, drift detection, and alerting systems for rapid issue resolution
• Collaborate with cross-functional teams in production, process engineering, controls, and quality to translate ambiguous problem statements into actionable, end-to-end machine learning solutions
• Perform model validation and benchmarking to ensure models maintain high accuracy and reliability in real-world factory conditions
• Follow agile development practices and maintain high standards for clean, modular, and sustainable production-ready code

Skills:

• In-depth knowledge of Python for high-performance, data-intensive applications
• Proficiency with at least one modern deep learning framework such as PyTorch, TensorFlow, or JAX
• Expertise in computer vision, including convolutional neural networks, object detection, segmentation, and image classification
• Experience processing and cleaning large-scale, multi-modal datasets (images, multi-spectral sensor data, video, tabular data)
• Foundational knowledge of statistics for model comparison, experiment design, and assessing solution feasibility and performance
• Real-world experience deploying and maintaining production machine learning systems, including monitoring and alerting
• Strong communication skills with the ability to translate technical results for non-technical stakeholders