Industrial Machine Vision Engineer (Optics, Lighting & Commissioning)
About this role
About the role
Gnutti Carlo Group Sweden deploys machine-vision inspection stations on high-volume manufacturing lines for die-cast and machined metal parts. This role owns the image acquisition layer: selecting and standardizing cameras, optics, and lighting, and commissioning stations so they are stable, repeatable, and fast to deploy across multiple lines.
Your mission is to deliver robust, production-ready imaging performance in demanding industrial environments (vibration, contamination, reflections, and thermal drift), with clear standards and repeatable procedures.
Scope and impact (what success looks like)
You will drive measurable improvements in imaging robustness and deployability, including:
Reduced commissioning time per station through reusable standards and procedures.
Higher first-pass acceptance on image-quality verification at handover.
Improved uptime / reduced re-adjustments (focus, alignment, glare control, lighting drift).
Lower imaging-driven false rejects through stable, quantified image quality.
Repeatability across lines via standardized BOMs, golden settings, and acceptance criteria.
Responsibilities
- Own imaging hardware selection: camera, lens, filters/polarization, illumination, and accessories; define station-standard BOMs including a spares strategy.
- Define “golden settings”: exposure, gain, strobe/triggering, and timing strategy with defined margins and drift tolerance.
- Create alignment and calibration SOPs: targets, reference images, setup steps, and verification methods for repeatable commissioning.
- Set measurable image-quality acceptance criteria: focus/MTF proxies, blur limits, glare control, SNR/contrast, uniformity, and ongoing drift checks.
- Specify mechanical interface requirements: mounting datums, adjustability, stiffness/vibration constraints, service access, cable routing, and strain relief.
- Commission on-site: bring stations to stable production operation and convert commissioning learnings into stand