Field Applications Engineer (FAE) – Manufacturing (Machine Vision & AI/ML)

Integrate solutions with PLCs, HMIs, robotics, and existing automation systems

Support connectivity with MES, SCADA, and plant network infrastructure

Optimize system performance for throughput, yield, and first-pass quality

Execute proof-of-concepts (POCs), pilot programs, and full production rollouts

Train operators, engineers, and quality teams on system operation and best practices

Develop documentation, SOPs, and troubleshooting guides

Support long-term adoption and continuous improvement initiatives

Act as a trusted technical advisor to manufacturing, quality, and operations teams

Translate production and inspection challenges into scalable technical solutions

Provide structured feedback to product and engineering teams to improve system performance and usability

Successful deployment and uptime of vision/AI systems in production

Model performance (accuracy, false positive/negative rates) in real-world conditions

Reduction in defects, scrap, or manual inspection

Improvements in throughput and overall equipment effectiveness (OEE)

Customer satisfaction and repeat engagements

Bachelor’s degree in Engineering, Computer Science, or related technical field

3–8+ years of experience in manufacturing, industrial automation, or field engineering

Hands-on experience with machine vision systems (image formation) in industrial environments

Strong troubleshooting skills across hardware and software systems

Ability to travel frequently (50% or more), including time on factory floors

Experience with: Computer vision frameworks (e.g., OpenCV, deep learning-based tools) AI/ML model deployment in production environments PLCs (Allen-Bradley, Siemens) and industrial automation systems Industrial networks (Ethernet/IP, PROFINET, Modbus) Cameras, lenses, lighting, and image acquisition systems

Computer vision frameworks (e.g., OpenCV, deep learning-based tools)

AI/ML model deployment in production environments

PLCs (Allen-Bradley, Siemens) and industrial automation systems

Industrial networks (Ethernet/IP, PROFINET, Modbus)

Cameras, lenses, lighting, and image acquisition systems

Familiarity with: Data annotation tools and dataset management Edge computing or GPU-based inference systems Lean manufacturing, Six Sigma, or continuous improvement methodologies

Data annotation tools and dataset management

Edge computing or GPU-based inference systems

Lean manufacturing, Six Sigma, or continuous improvement methodologies

Strong hands-on expertise in machine vision and AI-driven inspection systems

Ability to troubleshoot and optimize systems in high-pressure production environments

Systems thinking across hardware, software, and data pipelines

Clear communication with operators, engineers, and executives

Adaptability in fast-paced, variable manufacturing conditions

Located near a major airport for necessary travel