Machine Learning Engineer Job Description

A Machine Learning Engineer is an artificial intelligence and data science career that is tasked with designing, creating, and deploying software systems used for automation. In essence, they make sure a machine can operate properly without human intervention. These professionals are situated in a unique position that lies between data science and software engineering, which makes this job incredibly layered and complex yet mentally rewarding. This overview will explain what a Machine Learning Engineer does, what they typically earn, and how to learn the skills required to land a job. 

What is a Machine Learning Engineer?

As a Machine Learning Engineer, you will work on intelligent machine learning models that are self-teaching and inevitably learn from data to function with minimal human interaction. Essentially, the Machine Learning Engineer is a specialized role that utilizes artificial intelligence to help businesses succeed in several ways. They work on numerous projects, handle large amounts of data, train models to operate in a certain way, deploy the models into their respective environments, and monitor the model’s performance throughout its lifetime. 

The regular responsibilities of a Machine Learning Engineer will vary depending on the day and the progression of each project. However, they are typically tasked with studying datasets and applying the insights to create and enhance machine learning algorithms. They will typically run tests, document their findings, and further train and retrain models as needed so they operate as intended. Depending on the size of the company, a Machine Learning Engineer typically has a manager who handles regular activities for machine learning and data science teams. That being said, they often work closely with others in relevant positions, such as Software Engineers, Data Scientists, and Project Managers.

As it’s common to find Machine Learning Engineers collaborating with others on projects, it is no surprise that they frequently work in small teams and participate in larger meetings across different departments. In these meetings, they share information related to their work and keep others updated on their progress. Plus, from their collaborative efforts, they are better able to align their work with the needs of the company. Many Machine Learning Engineers work in a traditional office setting, however, there are many opportunities for these professionals to work remotely. They use different tools, programming languages, and digital skills that make their job possible, which makes this an incredibly complex yet rewarding position to land.

Job Requirements

There are many base-level requirements to become a Machine Learning Engineer. First, they must have strong knowledge of software programming languages, such as Java and Python as well as relevant libraries and frameworks. In addition, they understand software architecture, which essentially refers to the structure, organization, and behavior of a software system. This can involve knowing how to organize information within a software system, including specific interactions and the actual environment in which it operates.

Additionally, a Machine Learning Engineer will need strong mathematical skills. They will also use their well-developed communication skills for collaborative purposes, as they often work with others and will need to clearly explain their findings to stakeholders and their Reporting Managers. With this, a Machine Learning Engineer will need strong analytical skills to determine results from tests and use their innovative thinking processes to troubleshoot and solve problems.

Specific educational requirements will vary. A Machine Learning Engineer can hold degrees or certifications, but regardless, this job position will require some formal training. If you choose to pursue a career as a Machine Learning Engineer, you can pursue a bachelor’s degree in a related field, such as data science or computer science. While a degree in a relevant field is useful, but not exactly necessary. You can enroll in certificate courses, bootcamps, and other learning methods as a means to becoming a Machine Learning Engineer.

Job Responsibilities

• Designing machine learning systems and deploying them for production
• Acquiring, collecting, and processing data from different sources
• Analyzing and translating data into actionable insights
• Running tests on machine learning models to determine accuracy and documenting the results
• Deploying machine learning models for production and ensuring the efficiency of the models once they have become part of an application or platform.
• Collaborating with Project Managers, Data Scientists, Software Engineers, and other relevant professionals
• Monitoring the progress of previous machine learning models and updating them as needed

Skills

Among the wide variety of topics to study, a Machine Learning Engineer will need to understand complex mathematical and computer skills, specifically in statistics, probability, calculus, and linear algebra. This is fundamental for machine learning algorithms and allows the engineer to create, analyze, and enhance models as needed. Even more specifically, Machine Learning Engineers need to know hypothesis testing, differentiation and derivatives, and quadratic equations.

They will also have to learn programming languages like Python, R, and Java and their relevant machine learning libraries and frameworks such as Keras, NumPy, PyTorch, TensorFlow, and Scikit-learn. These are needed to write code for and inevitably produce machine learning models. It’s also essential for machine learning algorithms and deep learning architectures used in information automation. 

Above all else, Machine Learning Engineers need to continually develop their analytical, problem-solving, and communication skills. They must work well with others for collaborative purposes and understand the wide range of techniques that can be used to develop machine learning algorithms. All in all, a successful Machine Learning Engineer has a wide variety of skills and regularly updates their knowledge through continued education and experience.

Why Do Businesses Need Machine Learning Engineers?

Machine Learning Engineers are necessary in various businesses and fields as they play a crucial role in the success and continued growth of the company. By utilizing their advanced knowledge of data and artificial intelligence they can make more data-driven decisions, solve complex problems, innovate new solutions, secure domains from fraudulent activity, and personalize the user experience. A Machine Learning Engineer can enhance the customer experience and optimize business practices, which leads to more profitable and successful business processes.

Overall, Machine Learning Engineers optimize business procedures and help keep the business afloat in the competitive market. They are often responsible for preventing and detecting cybersecurity threats and mitigating risks, which is pivotal for companies that rely heavily on technology for their success and promotion. A Machine Learning Engineer will continually improve and refine the models they’ve deployed and provide invaluable technical expertise to the business for years following.

Where Do Machine Learning Engineers Usually Work?

Technology

Machine Learning Engineers can easily find work in the tech industry developing artificial intelligence-driven products. This may include developing software or creating cybersecurity measures. In addition, machine learning in the tech industry can include things like video, image, and voice recognition, which can allow for more hands-off interactions between computer systems and humans. This may also link to cybersecurity as the machine learning models can analyze user behavior, identify deviations in patterns, and identify flaws or anomalies during regular surveillance. Machine learning is integral to the tech industry and is seen as one of the most innovative and useful yet complex roles across different industries and fields.

Finance

In addition, machine learning is essential for financial institutions. Machine Learning Engineers develop predictive models that assist with everything from stock market patterns and portfolio management to fraud detection and customer service channels. These professionals enable the institution to handle and process large data sets from which actionable insights can be taken. The models may be deployed for personalizing the consumer experience, analyzing market data, following transaction patterns, and assessing the credibility of an individual or business based on their past credit history.

Retail and E-commerce

Machine Learning Engineers deploy models that ultimately clarify what the consumer needs from a retailer. In particular, machine learning professionals automate systems that make personalized consumer recommendations, analyze their behavior, and predict the company’s most optimal next steps. In addition, machine learning is used to manage inventory and adhere to a company budget. Overall, they enhance the day-to-day business operations and make the shopping experience stress-free for the consumer.

Machine Learning Engineer Salary and Job Outlook

As a Machine Learning Engineer, you can find employment across any industry or field utilizing data and automation for business practices. Sources across the internet will differ in the specific salary range, but the Bureau of Labor Statistics suggests that professionals working in the computer and information technology sector earn a median annual wage of $104,000. This will vary depending on the specific job title. For example, a Data Scientist can expect a yearly salary of nearly $108,000 whereas a Software Developer can earn roughly $130,000. These jobs are closely related to Machine Learning Engineers, so it is safe to assume their expected salary is somewhere in this range.

With an increasingly heavy reliance on data and technology and the rising use of artificial intelligence to maintain an advantage over the competition, Machine Learning Engineers hold a position that is expected to be highly sought-after and needed in companies across virtually every industry. Specifically, industries such as retail, technology, and manufacturing have long relied on Machine Learning Engineers, but even finance, healthcare, and educational institutions require machine learning experts to keep production moving. With the diverse set of knowledge and job functions a Machine Learning Engineer has, this is an incredibly worthwhile skill set to learn.

Plus, Machine Learning Engineers have an advantage in how they work on the job. They can find employment in a typical office setting, as many do, where they will work with other professionals on various projects. However, there are many remote or hybrid opportunities as well that allow for more flexibility. Machine Learning Engineers can even work as freelancers and offer their skills to multiple companies and build a well-equipped arsenal of professional connections.

How Long Does it Take to Become a Machine Learning Engineer?

The time it takes to become a Machine Learning Engineer will likely vary depending on your learning needs, educational path, and previous or relevant experience. There are many ways to become a Machine Learning Engineer, such as in a traditional degree program or courses at a technical training center. Earning a degree from an accredited university will be the longest option, generally lasting between two and four years. However, if you opt instead for a certificate program or any other kind of professional training journey, you can expect to spend varying lengths of time in class. Most certificate courses last a few weeks to a few months. Here, you gain practical skills as opposed to learning theory. Even then, it may take a solid one to two years of consistent work and exposure to the field before you can consider yourself a true Machine Learning Engineer.

Machine Learning Engineer vs. Similar Career

Machine Learning Engineers are closely related to Data Scientists, as they have many overlapping skills, job functions, and qualities between the two. For instance, both careers require knowing how to code, handle data sets, and design machine learning algorithms. The differing aspects lie mainly in their focus. Machine Learning Engineers focus on the design and deployment of machine learning systems whereas a Data Scientist is mainly involved in the collection and analysis of data. Additionally, a Data Scientist may know how to deploy machine learning models, but they value the analytical aspects more than the literal deployment and continued enhancement of the system.

Learn the Skills to Become a Machine Learning Engineer at Noble Desktop

If you are interested in becoming a Machine Learning Engineer, there are many ways to start your journey. Notably, you can explore certificate programs at reputable training centers such as Noble Desktop. Their Data Science Certificate covers all the necessary skills to become a data science professional, which is the field where machine learning practices are regularly applied. Specifically, students will learn relevant coding languages, automation, machine learning, data analysis, and predictive modeling skills. Throughout this 114-hour course, students will learn how to clean, balance, and apply machine learning algorithms to data sets with scikit-learn. In addition, they will practice analyzing data with Python libraries like NumPy and Pandas. By the end of the course, students will be able to automate everyday tasks and utilize their skills in the data science field. Noble Desktop provides students with additional benefits to support them through their learning journey. In this course, students can access 1-on-1 mentoring sessions, a free retake, and various payment plans. Upon completion, they will earn a verified digital certificate that displays their mastery of the material.

Likewise, Noble Desktop offers a Software Engineering Certificate where machine learning skills and relevant information will be discussed. In this certificate course, students will learn to use Python and Django for machine learning algorithms and task automation. In addition to Python, they will learn to build the front end of a webpage with HTML, CSS, and JavaScript, which all add interactive functionality. This course also gives students the exposure and experience they need to develop a professional portfolio with their completed projects. Over 510 total hours, students can access myriad benefits like a course retake, bonus mentoring sessions, and an additional elective course, all at no extra cost. Once the students complete the program, they will earn their verified certificate to show for all their accomplishments.

To gain more knowledge of artificial intelligence for business purposes, consider the Generative AI Certificate from Noble Desktop. In this 78-hour course, students will learn how to utilize artificial intelligence in different work settings and for varying reasons. For instance, students will learn how artificial intelligence can help automate workplace tasks or quickly analyze large data sets. The instructor will guide students through projects that leverage artificial intelligence for different marketing strategies and help create high-quality marketing designs. This course does not require any previous coding experience and is solely meant to delve deeper into the larger implications of artificial intelligence and its usefulness in the workplace. Through hands-on projects, students will gain the necessary experience to apply the skills in their field. Students can retake the course anytime within a year of their original enrollment at no additional cost. Plus, Noble Desktop will award students with a digital certificate once the course ends.