Manufacturing Innovation Advanced Technology Engineer

Advanced Technology Engineer - Vision & Edge AIRole Overview The Advanced Technology Engineer will develop and deploy AI-powered machine vision systems for defect detection and quality inspection in high-volume manufacturing environments. The primary focus of this role is building production-ready computer vision models, optimizing them for real-time edge hardware, and integrating them into manufacturing systems. Primary ResponsibilitiesModel Development & Training Acceleration Design and implement computer vision models for defect detection, segmentation, and classification. ccelerate training cycles using synthetic data, active learning, and domain randomization to address rare defects and specification variance. Production Deployment Package models and services using Docker and manage deployments through Kubernetes or equivalent orchestration tools. Implement version control, rollback strategies, and monitoring for latency, model drift, and false-positive/false-negative metrics. Edge Optimization Optimize inference for edge and embedded hardware (e.g., NVIDIA Jetson, Client accelerators) to meet strict real-time latency requirements for moving-line inspection. Ensure consistent performance under varying lighting, optics, and surface conditions. Integration with Manufacturing Systems Integrate vision systems with PLCs, encoders, triggers, and industrial networks using OPC-UA, MQTT, and REST protocols. lign deployments with plant-level architecture and connectivity standards to ensure reliability and scalability. Data Strategy & Quality Control Lead data collection campaigns and manage annotation workflows. Establish quality gates for model validation. Utilize synthetic data pipelines and augmentation techniques to improve robustness and reduce training time. Reliability & Sustainment Ensure uptime and availability targets through proactive monitoring, calibration (MSA), and backup/restore processes. Implement drift detection, audit false-out risks, and perform root cause analysis for inspection failures. What You'll Be Doing Develop and deploy production-grade machine learning models for industrial vision inspection systems. ccelerate model development using synthetic data and advanced AI techniques. Deliver containerized software optimized for edge hardware. Lead projects from concept through launch, including scheduling, milestone tracking, and cross-functional coordination. Evaluate new technologies in manufacturing environments and build business cases for adoption. Collaborate with internal engineering, IT, automation, and production teams to integrate robust AI solutions into high-volume manufacturing. Required Qualifications Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer Science, IT, or related field. 5+ years of experience in industrial machine vision and edge AI deployment. Strong proficiency in Python and C++. Experience with ML frameworks (PyTorch, TensorFlow). Hands-on experience with Docker and Kubernetes. Familiarity with ONNX Runtime, TensorRT, and embedded optimization. Experience integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT). Experience managing the full AI lifecycle: data collection, labeling, validation, rollout, monitoring, and retraining. Knowledge of object detection, classification, and segmentation models. Experience with industrial cameras, lighting, and trigger-based image capture. Preferred Qualifications Master's degree or advanced engineering degree. Experience deploying automotive or high-volume production equipment. Robotics experience (operation, teaching, maintenance, safety). Expertise in synthetic data generation (GANs, VAEs, NeRFs, Blender) and domain randomization. Experience with high-speed inline inspection systems and IIoT data pipelines. Strong understanding of calibration, MSA, PFMEA, and quality-critical inspection requirements. Key Competencies bility to deliver production-ready AI solutions under strict timelines. Strong cross-functional collaboration and project leadership skills. Commitment to quality, reliability, and continuous improvement in manufacturing environments.

Created: 2026-03-04