Applications Engineer, Overview

• 1 to 3+ years in machine vision, computer vision, applications engineering, robotics, automation, manufacturing, or a related field
• Strong hands on grasp of machine vision fundamentals: cameras, lenses, optics, lighting, and image acquisition
• Familiarity with lens types and when to use them, including telecentric lenses
• Working understanding of AI based vision and how it differs from traditional rules based vision
• Familiarity with image labeling, model training, classification, object detection, and segmentation
• Strong troubleshooting instincts and a logical, experimental approach to technical problems
• Comfortable working hands on with hardware and testing approaches independently
• Able to document and communicate technical findings clearly
• Eager to learn new technologies quickly, including industrial automation and PLCs
• Based in San Francisco, working primarily from our office, with occasional travel to customer sites

Desirable

• This is a hands on machine vision role for someone who wants to get deep, fast
• You will split your time between the lab and live customer applications: evaluating a lens, optimizing a camera setup, building a proof of concept, troubleshooting an inspection that has to hold on a production line
• The work moves from bench to factory floor in weeks, not quarters, and what you learn in the lab shows up directly in what we deploy
• Become a genuine machine vision expert: optics, lighting, sensors, and AI inspection, on real parts and real lines
• Own the experiment. You decide how to test it, you write the report, the finding changes the deployment
• Sit at the seam of lab research and customer reality, where most vision engineers only ever see one side
• Work directly with the most demanding manufacturers in the world, with Sales, Product, and Engineering a desk away
• Lab testing and experimentation: Hands on testing with cameras, lenses, lighting, and other machine vision components
• Vision setup builds: Build and evaluate vision configurations for customer applications and internal projects
• Technical test reports: Run application tests and turn out clear, fast technical reporting others can act on
• Technology evaluation: Assess new components and technologies and call whether they improve our systems
• Customer support in the field: Testing, troubleshooting, deployment, and technical problem solving for local customers
• AI vision workflow: Image collection, labeling, training, and model evaluation on AI based inspection systems
• Team enablement: Build the internal test setups, resources, and documentation the broader Applications team runs on
• Optical and hardware projects: Camera calibration, new optical configurations, and hardware evaluation