Machine Learning Engineering is an IT role concerned with deep learning. It 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 AI, artificial intelligence, which allows the machine to complete an intended task without being prompted to do so. Common examples of what Machine Learning Engineers 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 a Machine Learning Engineer, particularly when new to the field.

Most Machine Learning Engineering professionals have an advanced degree in computer science, an engineering discipline or statistics. Some employers may prefer their Machine Learning Engineers to keep an updated engineering license. 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 there programming, mathematics and science skills balanced are strong enough to develop computations 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

Machine Learning Engineer 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 Machine Learning Engineer 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.

Working as a Junior Level Machine Learning Engineer

Machine Learning Engineers at the junior level have demonstrated years of on-the-job experience, gaining expertise on the machine and deep learning technologies through teamwork and project-specific experiments. At the junior level, a Machine Learning Engineer will be expected to take on more responsibilities, perhaps even leading a junior team and may often help with training new hires.

Moving up the Ranks

By demonstrating a consistent progression in your work, a skilled Machine Learning Engineer can easily work their way up the career ladder, from entry-level, to junior and into senior ranks. 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. The ability to take reasonable risks and try out an experiment and not being dismayed if it fails will show how determined the Machine Learning Engineer is.

An ascent up the Machine Learning Engineer 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.

Advance Your Career: How to become a Senior Machine Learning Engineer

Machine Learning Engineers at the senior level have developed a mastery for deep learning and data science, but they do not rest on their laurels. The key to attaining a Senior Machine Learning Engineer position is to keep challenging yourself to pick up new skills related to technology and AI. Employers will likely promote Machine Learning Engineers who not only have the technical skills earned through years of work but those who also consider their colleagues and the company at large. Having an interest in mentoring junior colleagues and being able to communicate effectively with team members about your work is what senior Machine Learning Engineers do well.

Reaching a senior level will pay off in many ways, not only financially, but in the respect, the Machine Learning Engineer will command and the type of interesting work they will be expected to spearhead.

Study the Core Fields

The precise skills a Machine Learning Engineer may need may depend on the company they work for, though there are certain fields that all Machine Learning Engineers will likely need on the job. Computer programming skills are a must. A growing trend towards having an aptitude with Python, Java, C and C++ programming languages 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

Machine Learning Engineering 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.

Don’t Stop at Machine Learning Engineering

There are similar fields to Machine Learning, the understanding of which can make a Machine Learning Engineer an even stronger professional. Some of these roles include data and research scientists, computer vision engineers and business intelligence developers, all of whom study data patterns, set research experiments and create software and systems that help automate certain functions and make work and life easier. A Machine Learning Engineer who wants to keep their skills sharpened will take the time to study up on these professions and see what they can learn about the trial and error experienced in these roles.

Machine Learning Engineer Job Description

Machine Learning Engineers create new systems and machines that are powered with artificial intelligence to complete assigned tasks without prompting. Companies with Machine Learning Engineers are seeing incredible investments rolling in for their projects, as deep learning machines and technologies are being sought and utilized in virtually every sector. Machine Learning Engineers can find excellent employment working for healthcare companies that want to make uncovering diseases in patients quicker and more precise; in manufacturing, as companies continue to automate work to increase production and reduce injuries; in media, helping news organizations attract more readers by learning what topics pique their interests and programming related feeds to entice them to click and so much more.

Deep learning and AI are considered by experts as the wave of the tech future, with Machine Learning Engineers taking the helm and creating incredible intelligent devices.

What We Need Your Help With

• Engaging in data modeling and evaluation
• Developing new software and systems
• Designing trials and tests to measure the success of software and systems
• Working with teams and alone to design and implement AI models

We Look For

• An aptitude for statistics and calculating probability
• Familiarity in Machine Learning frameworks, such as Scikit-learn, Pytorch and Keras TensorFlow
• An eagerness to learn
• Determination – even when experiments fail, the ability to try again is key
• A desire to design AI technology that better serves humanity

These Would Also Be Nice

• Good communication – even with those who do not understand AI
• Creative and critical thinking skills
• A willingness to continuously take on new projects
• Understanding the needs of the company
• Being results-driven

Senior Machine Learning Engineer Career Paths: Where to Go from Here

Machine Learning Engineers at the senior level can enjoy a long and fruitful career where they are, helping to develop and deploy intelligent systems that guide the future and affect the daily lives of millions of people.

For those interested in a career change may consider such roles as senior data scientists, product managers and DevOps managers, as they rely on the same research, trial and error and dedication at which Machine Learning Engineers excel.

There is every reason for senior Machine Learning Engineers to stay where they are since it is a career that experts agree is growing and evolving. Millions of Machine Learning Engineer jobs are predicted to open over the next decade or so as automation is seen as the way for businesses across all sectors to expand and improve their operations. Being there as new technologies take off is exceedingly rewarding and staying ahead of the curve is the secret to lasting success.