Advanced Process Controls Engineer (MPC & ML)

Corning Incorporated; Painted Post (NY); On-site; USD 71,000 - 98,000; Full time

Benefits offered by this job

Company bonuses

401(k) matching

Health benefits

Paid time off

Job summary

Corning Incorporated is seeking an Advanced Process Controls Engineer to apply MPC and ML methods to improve manufacturing efficiency. The role spans model-based control, optimization, and data analytics across Corning's diverse processes.

You will work with cross-functional teams to implement MPC-based solutions, tune PID loops, and develop scalable optimization models for production planning and scheduling. This position requires a MS in engineering and 1–3 years of relevant experience.

Qualifications

• MS degree with coursework in advanced controls or analytics.
• 1–3 years of manufacturing/process experience preferred.
• Experience with MPC and PID control; model-based design.

Responsibilities

• Design and implement process control solutions for manufacturing.
• Evaluate and enhance control strategies including MPC and IMC.
• Develop MPC-based solutions and optimization models for planning.
• Collaborate with manufacturing to select hardware/software platforms.
• Apply data analytics and ML to improve process understanding.
• Translate business logic into mathematical models and constraints.
• Support transfer of new tech to production and document IP.

Skills

MPC Control

PID control

Optimization

MATLAB/Simulink

Python

Machine learning

Data analytics

Problem solving

Team collaboration

Education

M.S. in Electrical/Mechanical/Chemical Engineering

1–3 years industry experience

Tools

Gurobi

Pyomo/AMPl/PuLP

Job description

Corning Incorporated is seeking an Advanced Process Controls Engineer to apply MPC and ML methods to improve manufacturing efficiency. The role spans model-based control, optimization, and data analytics across Corning's diverse processes.

You will work with cross-functional teams to implement MPC-based solutions, tune PID loops, and develop scalable optimization models for production planning and scheduling. This position requires a MS in engineering and 1–3 years of relevant experience.