Mechatronics Engineer: Lab Prototyping & Automation

Corning Inc.; Hickory (NC); On-site; USD 85,000 - 117,000; Full time

Job summary

Corning Inc. in North Carolina is seeking a Mechatronics Engineer to join our early-stage development team.

In this hands-on role, you’ll design, build, and test experimental setups for laser processing, adhesive bonding, and precision dispensing, advancing manufacturability concepts. You’ll work in a collaborative environment with cross-disciplinary scientists and engineers, performing DOE‑driven experiments, analyzing results, and documenting findings to guide scalable process knowledge for

Qualifications

• BS or MS in a relevant engineering field with hands‑on lab experience.

Responsibilities

• Design, assemble, and test benchtop and lab systems for process characterization and concept Validation.
• Plan and execute experiments to define process windows and critical variables.
• Analyze data using DOE, hypothesis testing, and FMEA to guide design decisions.
• Document methods, results, and conclusions for technical reviews and IP opportunities.
• Collaborate with scientists and engineers to translate early concepts into scalable processes.
• Model safe work practices and contribute to a safety culture in the lab.

Skills

Python

MATLAB

C++

C#

Lab instrumentation

Written communication

Education

BS in Mechanical Engineering, Mechatronics, Electrical Engineering, Optical Engineering, or related field

MS in a related discipline with internship/research is a plus

Tools

SolidWorks

PCB layout

Machine vision

Job description

Corning Inc. in North Carolina is seeking a Mechatronics Engineer to join our early-stage development team.

In this hands-on role, you’ll design, build, and test experimental setups for laser processing, adhesive bonding, and precision dispensing, advancing manufacturability concepts. You’ll work in a collaborative environment with cross-disciplinary scientists and engineers, performing DOE‑driven experiments, analyzing results, and documenting findings to guide scalable process knowledge for