AdaptiLab Blog

How to Become a Machine Learning Engineer

Posted by Allen Lu on Jan 7, 2020 3:16:00 PM

The role of a machine learning engineer (MLE) involves taking on both machine learning and software-related tasks. The MLE role is projected to continue its rise in popularity as more industries begin to understand the value of investing in deep learning technologies, which is seen particularly in media, healthcare and manufacturing sectors. These engineers are adept at creating technologies embedded with artificial intelligence (AI), which allows the machine to complete an intended task without being prompted to do so. Common examples of what MLEs work on include self-driving cars for Uber and programming tailored search results for Google users. Machine learning utilizes the most cutting-edge technology, so professionals in this field must have the desire to seek and master new technologies as they emerge.

Getting Through the Door

An entry-level machine learning engineer will design and build models, often on a team, that follow data science and AI technologies. They will be expected to understand the needs of the business and conceptualize projects that meet business goals. Being tech savvy and having a desire to create automated technologies that make life easier should always be the goal of an MLE, particularly when new to the field.

Most MLEs have an advanced degree in machine learning, computer science, statistics, or some other engineering discipline. The type of education and skills a potential employer may keep in mind when considering hiring an entry-level machine learning engineer can include:

  • Have they attained a master's or a doctoral degree in a related discipline?
  • Do they demonstrate a proficiency in current and emerging technologies?
  • Are they able to problem solve and write sophisticated programs and algorithms?
  • Do they have an interest in keeping their skills sharpened by learning new programs and languages that complement and enhance their machine learning toolkit?

Degrees and Experience

MLE work is highly sophisticated, so professionals in this field need a high level of education and technical experience to advance their career. In most cases, a bachelor's degree is not enough and many employers expect MLE candidates to have a master's or Ph.D. in computer science, an engineering discipline, or mathematics.

Experience is also key to becoming a successful machine learning engineer. These engineers gain experience by working on practical and theoretical models to enhance and demonstrate their hands-on skills. This is a field like many other scientific and technical roles, as there is plenty of trial and error.

Entry-Level MLE

Entry-level machine learning engineers often have existing work experience in engineering or machine learning, often accompanied by a master's degree in a relevant field. On the job, entry-level MLEs gain expertise in machine and deep learning technologies through teamwork and project-specific experiments. An entry-level MLE will be expected to handle various different responsibilities, from creating machine learning models to building out data pipelines.

Moving Up the Ranks

By demonstrating a consistent progression in your work, a skilled MLE can easily work their way up the career ladder, from entry-level to more senior-level roles. This may occur by being a great team player and working well on cross-functional teams. Following trends in technology and learning new skills will also signify to your employers how curious you are. Improving the quality of your work to beyond what is expected will show how dedicated you are to your job.

An ascent up the MLE career path takes fortitude, a desire to learn and being an excellent team player. With these skills in mind, there is every reason why moving up in the ranks should be possible.

Getting Started

Study the Core Fields

The precise skills an MLE may need may depend on the company they work for, though there are certain fields that all MLEs will likely need on the job. Computer programming skills are a must. A growing trend towards having an aptitude with Python (and sometimes C++ and R) is also emerging. Studying where machine learning technology is being utilized, such as with self-aggregating newsfeeds and understanding the ins-and-outs of how this technology is created can help build basic AI skills. And finally, following deep learning trends in the industry can help machine learning engineers understand the future of their careers.

Invest In Yourself

The MLE role is still a relatively new career but is quickly building traction as a field set to change the face of the world. Engineers in this field should strive to stay on top of emerging technologies and industry trends to stay relevant. This can be done by taking additional courses, either online or at a special technical school, as well as attend seminars and conferences, network and keep an eye out for literature pertaining to movements within the industry.

Expand Beyond General ML

Beyond just staying in the realm of general machine learning and deep learning knowledge, smart machine learning engineers will expand their skills to other fields that utilize machine learning. These fields include natural language processing, computer vision, and business intelligence. An MLE who wants to keep their skills sharpened will take the time to study up on these fields and see what they can learn.

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