Engineer I & II

Corning•Town of Erwin, NY

46d•$71,067 - $97,718•Onsite

About The Position

Corning's Manufacturing, Technology and Engineering division (MTE) is recognized as the leader in engineering excellence & innovative manufacturing technologies by providing diverse skills to Corning’s existing & emerging businesses. We anticipate & provide timely, valued, leading edge manufacturing technologies and engineering expertise. We partner with Corning’s businesses and the Science & Technology division. Together we create and sustain Corning’s manufacturing as a differential advantage. MT&E is seeking an Advanced Process Controls Engineer who believes that cutting-edge controls, optimization, and machine learning technologies can improve manufacturing processes to deliver competitive advantage. This is one of the most versatile technical roles within Corning, with broad scope across the wide variety of processes in Corning's businesses. The field of process controls is evolving from traditional single-input/single-output sensing and control to complex multi-input/multi-output problems that call for model-based strategies, most notably model predictive control (MPC), and large-scale numerical optimization for production scheduling, ware flow, and supply chain. In parallel, Corning is investing in machine learning for continuous improvement; the engineer will apply ML methods to process monitoring, fault detection, defect classification, and predictive maintenance.

Requirements

M.S. in Electrical, Mechanical, or Chemical Engineering (or related field) with coursework in Advanced Controls, Optimization, or Data Analytics.
1–3 years of industry experience on manufacturing processes. New graduates with related internship experience will be considered.
Familiarity with a diverse array of process controls technologies, including MPC and traditional PID control; practical PID loop tuning experience.
Controls-oriented modeling and system identification.
Foundation in numerical optimization (LP, NLP, MILP, convex optimization) and ability to formulate optimization problems from business and process logic.
Programming experience in MATLAB/Simulink and Python.
Background in machine learning classification and regression techniques.
Strong problem-solving, verbal, written, and interpersonal skills; effective presenter to technical and non-technical audiences.
Ability to work in a manufacturing environment.
Performs well on team initiatives and independent assignments; results-oriented with a track record of meeting commitments and managing time well.

Nice To Haves

Hands-on experience with MPC implementation, tuning, and maintenance.
Experience with large-scale optimization solvers (e.g., Gurobi) and modeling languages (Pyomo, AMPL, PuLP, etc.).
Multivariate statistical process control methods such as PCA and PLS regression.
Experience operationalizing ML-driven models in manufacturing environments.
Experience working with Git and version control systems.
Experience working in cross-functional and global teams.
Strong technical curiosity and desire to take on challenging technical problems.

Responsibilities

Design, develop, and implement process controls solutions for manufacturing processes, working in multi-disciplinary teams.
Evaluate existing control strategies and propose enhancements, from PID improvements (cascade, feedforward, gain-scheduling, loop pairing) to model-based algorithms such as MPC and internal model control (IMC).
Develop and maintain MPC-based solutions for assigned processes, including formulation of the underlying optimization problems.
Build and improve large-scale optimization models (LP, NLP, MILP) for production planning and scheduling.
Partner with manufacturing to define appropriate hardware and software platforms and implement solutions in existing PLC/DCS environments.
Develop in-depth knowledge of processes with subject matter experts; apply data analytics and machine learning to gain fundamental process understanding and identify control/optimization opportunities.
Apply multivariate statistical methods and machine learning to process monitoring, fault diagnosis and isolation, defect classification, and predictive maintenance.
Translate business processes and workflow logic into mathematical models, objective functions, and constraints.
Plan and execute assigned tasks and workstreams in a research, development, and engineering environment; contribute to proposals for new controls and optimization projects.
Support the transfer of new technologies into manufacturing and share knowledge with manufacturing and engineering personnel.
Generate intellectual property through technical reports, invention disclosures, and patent applications.