The SME, a professional association for manufacturing workers in the U.S. and Canada, offers free membership for high school students (as well as a paid option for college students). Membership benefits include access to the “SME Connect” blog, scholarships, competitions, and a mentorship program, as well as a subscription to Manufacturing Engineering magazine. Learn more at https://www.sme.org/sme-membership/join-sme.

Conduct an information interview with a manufacturing engineer to find out their educational background and how they got started in their career. Suggested interview questions include: What made you want to enter this career? What type of tools and equipment do you use to do your work? What do you like most and least about your job? How did you train for this field? What are some key skills for success? What advice would you give to young people about preparing for the field and being successful on the job? How will advances in technology change the field? Perhaps you could even job-shadow a manufacturing engineer to learn even more about the field. Ask your schools career services office for help with setting up these learning opportunities.

Join the Technology Student Association (https://tsaweb.org), a membership organization for engineering, science, and technology students that offers career development programs and competitions.

Participate in competitions that are offered by SkillsUSA (https://www.skillsusa.org) to build your skills, compete for scholarships and cash prizes, meet people with shared interests, and have fun. Some of the SkillsUSA competitions that will be of interest to aspiring engineers include Additive Manufacturing, Automated Manufacturing Technology, Engineering Technology-Design, Principles of Engineering-Technology, and Robotics and Automation Technology.

Get involved with your schools science clubs, participate in science fairs, and volunteer with local engineering organizations to learn more about the engineering field.

Manufacturing engineers apply scientific knowledge and principles for practical applications. They design, test, and improve manufacturing systems, processes, and products. They identify and solve operational problems and come up with solutions that increase efficiency and productivity while reducing costs. They work closely with other engineers, technologists and technicians, and commercial and industrial designers to enhance the quality, reliability, and cost-effectiveness of manufacturing products and processes.

Examples of what manufacturing engineers may be hired to do include the following: use computer-integrated technology to automate a chemical manufacturing facility; help a company improve product quality and reduce costs by designing a circuit board manufacturing process; or identify a cost-effective facility and material handling layout for an aerospace manufacturing company.

There are five areas of work that manufacturing engineers may be involved in: research, development, application, management, and maintenance. Projects start with research. Engineers who work in this area investigate new materials, processes, or principles for practical applications of ideas and materials. Development is next, and these engineers study research results to determine how best to apply them to their practical functions. Application is the actual production of a developed idea. Engineers design and produce the materials, machines, methods, or other results of research and development. Engineering work is finalized with management and maintenance, and these engineers keep the developed idea working.

Manufacturing engineers use various software programs to gather and review information, develop and fine-tune designs, and create reports and other documents. Some examples of the programs they are well versed in include computer-aided design, computer-aided manufacturing, development environment, enterprise-resource planning, project management, and computer numerical control software. Much of this software is powered by advanced artificial intelligence (e.g., machine learning, deep learning, computer vision, natural language processing).