A blend of education, skills and experience is necessary to become a machine learning engineer. Here’s one path you can take:
According to the U.S. Bureau of Labor Statistics (BLS), computer and information research scientists who work with machine learning need at least a master’s degree in computer science or a related field. This can include a Master of Science in Computer Science or, if you’re looking to become a data scientist, a Master of Science in Data Science, since machine learning is a subset of the field.
To meet the standards of most machine learning roles, you’ll need a set of certain hard and soft skills. At minimum, you’ll need:
- Knowledge of programming languages
- An understanding of technical subjects involved in machine learning
- Proficiency in advanced math
- An advanced understanding of AI and machine learning software
Hiring managers will also look at your personality, which should be supported by soft skills, such as:
- Attention to detail — Even the smallest amounts of code or data can affect machine learning software. As an engineer, you are expected to spot these small details regularly.
- Analytical skills — Machine learning means analyzing and organizing data to develop AI programs. This requires a fair level of analysis that you should build through your education and work experience.
- Problem-solving — As an engineer, you’ll inevitably run into problems with machine learning and other AI systems. Knowing how to problem-solve will help you stay calm and address any kinks along the way.
- Teamwork and communication — Since machine learning engineers work with a team of other engineers and IT specialists, it’s important to know how to communicate and work with others.
Once you have a relevant degree and skills, you can begin to apply for entry-level positions. When doing this, it’s important to find ways to stand out from your competitors. One such way is to build up your experience with machine learning so you can list it within your resumé. This can be anything from shadowing experience with other machine learning engineers to an internship.
Although this experience may not guarantee you a position, it will highlight your knowledge of a machine learning working environment and any skills you developed during that time.
Machine learning is a field that will continue to grow as long as technology continues to develop. It will require engineers who are open to continual learning throughout their career. Being willing to adapt, grow and learn are important aspects to working in the field of technology.
Information technology at University of Phoenix
While University of Phoenix does not educationally prepare students to become machine learning engineers, there are several information technology degrees to consider if IT or data science interests you.
- Bachelor of Science in Computer Science — This program equips you with the knowledge to apply information technology theory and principles to address real-world business challenges with advanced concepts in math, programming and computer architecture. You can also use elective courses to earn a certificate in cybersecurity, networking, cloud computing and much more.
- Bachelor of Science in Information Technology — Learn skills pertaining to information systems, system analysis, operations and cybersecurity.
- Bachelor of Science in Data Science — Gain fundamental skills and knowledge for analyzing, manipulating and processing data sets using statistical software. Learn ETL (extract, transform, load) processes for integrating data sets for business intelligence. Focus on data mining and modeling, data programming languages, statistical analysis, and data visualization and storytelling. Discover techniques to transform structured and unstructured data sets into meaningful information to identify data patterns and trends and drive strategic decision-making.
- Master of Science in Data Science — In this program, you will learn how to analyze, design and manage data sets and models used to optimize functionality and scalability and improve business system performance. Learn database design, data processing and warehousing, data queries and interpretation, business intelligence and statistical methods, as well as how to apply data science strategically to improve business decision-making.