Reliability Engineering Co-op: Hands-on Maintenance

Ingredion; Indianapolis (IN); On-site; USD 30,000 - 36,000; Full time

Benefits offered by this job

Housing stipends for relocation by 51+

Intern Mentor Program

Capstone Summit at Ingredion HQ

Employee recognition and rewards

Job summary

Ingredion is seeking a Reliability Engineering co-op for the May–December 2027 term in Indianapolis, IN. The role offers hands-on exposure to manufacturing, corn wet milling, and reliability technologies while you learn equipment strategies and condition monitoring.

You will work with operations and maintenance to study processes, perform PM optimization, and support root cause analysis of failures, with emphasis on sensors, AI, and data analytics.

Qualifications

• Progress towards a bachelor’s degree in Mechanical or Electrical Engineering; Chemical Engineering considered with GPA 3.0/4.0 preferred.
• Must be enrolled in a Bachelor’s or Master’s program; if undergrad, completed at least two years of coursework.

Responsibilities

• Assist operations and maintenance to learn and understand processes.
• Support continuous improvement of maintenance systems and PM optimization.
• Contribute to root cause analysis of equipment failures.
• Help develop criticality-based equipment strategies and maintenance routes.

Skills

Self-motivated

Proactive

Hands-on & computer skills

Engineering student (Mech/Electrical)

GPA 3.0+

40 hrs/week availability

Technical communication

Data analytics

Sensors & AI interest

SAP knowledge

Education

Mechanical or Electrical Engineering (bachelor’s)

Chemical Engineering considered

Tools

SAP

Job description

Ingredion is seeking a Reliability Engineering co-op for the May–December 2027 term in Indianapolis, IN. The role offers hands-on exposure to manufacturing, corn wet milling, and reliability technologies while you learn equipment strategies and condition monitoring.

You will work with operations and maintenance to study processes, perform PM optimization, and support root cause analysis of failures, with emphasis on sensors, AI, and data analytics.