Machine Learning Engineers
Tech companies—such as Facebook, LinkedIn, Amazon, Apple, SAP, Google, and Adobe—are the main employers of machine learning engineers. Others work in the IT or product development departments of companies in the banking, transportation, shipping, accounting, health care, agriculture, security, and human resources sectors, among others. Machine learning engineers also work in the U.S. military or for government agencies. Others work as computer science teachers at the high school and college levels.
Machine learning is a relatively new field and, as a result, there is a shortage of skilled professionals. As a result many existing software engineers, programmers, computer scientists, data analysts, and other information technology workers are “upskilling” their credentials and applying for machine learning positions. They do so because of the high salaries, the excellent employment outlook, and the opportunity to work in an exciting industry with many different career paths and employers.
If you’re in college you can learn about machine learning jobs by participating in internships and other experiential learning opportunities. If you impress the internship manager, you may be offered a full-time job after graduation. Additionally, use the resources of your school’s career services office to help find a job. Career counselors can help you spruce up your resume, improve your interviewing skills, and steer you toward online and in-person career fairs, as well as internship and co-op programs. You should also visit the Web sites of target companies to learn about their products and services, what types of skills and educational training are required for their MLEs, and to apply for jobs. Participate in hackathons or open-source data science competitions because these are good ways to build your skills and network, but also because many companies use these contests to identify promising job candidates.
A machine learning engineer with considerable experience and skill can advance to become a project manager, who oversees the work of a team of MLEs, data science professionals, and other workers. The next advancement levels would be to director of AI and then chief technology officer. Some MLEs launch their own software development companies or consulting firms. Others become college professors.
Tips for Entry
Participate in information interviews with machine learning professionals. Women in Machine Learning offers a directory of female NL professionals at https://wimlworkshop.org/sh_projects/directory. Use this directory to find leads for potential information interviews.
Review machine learning job ads to learn what types of education, skills, and software expertise employers are seeking. These qualification will vary by company so it’s a good idea to identify what’s in demand.
Attend the annual Women in Machine Learning Workshop, https://wimlworkshop.org/about-wiml-workshop, to network and learn more about trends in the field.