A machine learning engineer helps research, build, and design automatic artificial intelligence systems to apply predictive models.
The field of machine learning engineering is only growing as companies look for more talent capable of creating automated artificial intelligence systems. This makes it an ideal career path for those with specialized interests and skills.
In this guide, we’ll take a deeper dive into the role of a machine learning engineer, including a look at the recommended skills background, and steps needed to become a machine learning engineer.
What is a Machine Learning Engineer?
Machine learning engineers apply the principles of mathematics and computer science to design and build AI systems based on predictive modeling. Predictive modeling is a mathematical process used to predict future events by analyzing patterns in input data.
There are many machine learning and automated artificial intelligence applications already in use, especially when it comes to digital media, communication, and technology. A common application of machine learning is facial recognition in social media images.
Social media platforms like Facebook and Instagram can predict which users the account holder may want to tag based on the people in the image. A second popular application of machine learning is speech recognition.
Machine learning can translate speech into text, and through a user’s guidance, software applications can convert live voice and recorded speech into text files with greater accuracy. The most famous speech recognition products are household devices like Google Home and Amazon Alexa.
The machine learning engineer’s role goes beyond basic computer programming and requires the creation of programs that will enable machines and software to perform tasks without being directed by humans.
In general, a machine learning engineer acts as a bridge between the statistical and model-building work and the building of robust machine learning and artificial intelligence systems, platforms, and services.
As a real-world example, machine learning engineers can help financial professionals predict whether a transaction is fraudulent or legitimate. Predictive modeling can allow machine learning to classify input data into multiple groups, which rules set by the analyst then define.
After the software organizes the data into stratified groups, analysts can calculate the probability of an error. Engineers work to improve prediction systems to calculate the possibility that an error has occurred, making everyday life easier for consumers and company leaders.
Within this role, machine learning engineers are responsible for implementing isolated statistical analysis, machine learning, and artificial intelligence data into high-performance, accessible systems that provide quick and easy access to end-users. Only a small portion of the machine learning engineer’s job is writing the actual machine learning code.
The rest of the project includes configuration, data collection, data verification, feature extraction, analysis tools, process management tools, machine resource management, serving infrastructure, and monitoring, which support structures necessary to run the machine learning code itself.
What Machine Learning Degrees Are Needed?
Machine learning engineers are expected to have at least a master’s degree and occasionally a Ph.D. in artificial intelligence, machine learning, or data analytics. Advanced knowledge in mathematics and data analysis is critical in a machine learning engineer’s background in the digital age.
Many schools provide students with a master’s degree in machine learning, including Carnegie Mellon University, Cornell University, Georgia Institute of Technology, Duke University, Massachusetts Institute of Technology, Boston University, University of Rochester, UCSD, Stevens Institute of Technology, and Stanford University.
Many programs combine machine learning with another emphasis, such as data science or analytics, such as the programs at Duke, Rochester, and MIT. Regardless of the program, most master’s level degrees allow students to get hands-on experience with computer science, artificial intelligence, and data analytics, which are foundational concepts to a machine learning career.
How to Become a Machine Learning Engineer
The first step towards becoming a machine learning engineer is building up the required knowledge and experience. Employers typically look for two things here:
Many job descriptions for machine learning engineers require applications to hold a master’s degree or higher in computer science specializing in machine learning, data analytics, or artificial intelligence.
One reason for this is that machine learning engineers are expected to apply computer science theory to a higher level than expected of bachelor’s degree recipients. A master’s degree demonstrates that an applicant has a familiarity with advanced theory and the necessary skills in coding and project management.
Although a Ph.D. shows further interest in this specialized subject, most machine learning engineers on Kaggle (an online community for machine learning and data science) have disclosed the master’s degree as their highest level of education.
Many employers want to see some evidence of experience with software development in addition to specialized experience with machine learning and artificial intelligence systems.
Most respondents have between 3 and 10 years of experience, skewing young (between 25 and 35 years old). Internships or participation in events like bootcamps or hackathons can also be used to demonstrate experience.
Although there is an increasing number of machine learning job candidates learning prerequisite information on their own, a master’s degree from an accredited institution is typically expected.
Machine learning engineers also need to have the ability to both design and create artificial intelligence applications, which requires knowledge of:
- Programming Languages: Machine learning engineers are developers and are expected to know how to code at an advanced level. Although the necessary language(s) to learn depends on an applicant’s desired role, the five best languages for machine learning are Python, R, Java, Julia, and LISP.
- Machine Learning Model Training Tools: The top tools used for machine learning model training include TensorFlow, PyTorch, Scikit-learn, Catalyst, XGBoost, LightGBM, and CatBoost. Machine learning engineering candidates should be proficient in at least one of these tools, preferably the one listed in the job advertisement.
- Development and Design Methodologies: Machine learning engineers are expected to design and architect automated applications based on predictive modeling. Machine learning can be simplified into seven major steps: collecting data, preparing the data, choosing a model, training the model, evaluating the model, tuning parameters, and making predictions.
The top-rated companies hiring machine learning engineers in the United States are:
- NYC Data Science Academy
- General Assembly
- Ford Motor Company
Bayer, IBM, General Assembly, Ford Motor Company, and Kmart are known for having the highest compensation packages and the best work-life balance, according to public surveys on Indeed.com.
It can be challenging for candidates to prove that they have the required experience and skills during an interview. To help them gauge a candidate’s experience, employers typically look for:
- Portfolio: Employers are increasingly looking for samples of an applicant’s work during the interview process. Having a Github repository with some past automated ML/AI work can help you get a job as a machine learning engineer.
- Certifications: Like many technology roles, machine learning engineers are expected to engage in continuing professional education. Earning certifications and demonstrating skills with relevant platforms, languages, and design methodologies can help meet these needs when developing new skill sets.
Hard skills are not enough to be successful as a machine learning engineer. Machine learning engineers also need certain soft skills, such as:
- Organization: Machine learning engineers are frequently responsible for bridging data analysts to end-users through clean code and complex automated systems. Doing so effectively and correctly requires machine learning engineers to be organized and detail-oriented, balancing the knowledge and needs of multiple parties.
- Communications: Machine learning engineers need to be able to work together with data analysts, software engineers, developers, customers, and project stakeholders. A machine learning engineer must have strong communication, collaboration, and project management skills.
What Does a Machine Learning Engineer Do?
At the core, the job of a machine learning engineer is to create automated artificial intelligence systems. However, this can include a few different responsibilities, such as:
- Translation: Machine learning engineers may be responsible for translating the work of data scientists from environments such as Python and R into other applications that are more accessible to the end user.
- Web services: Machine learning engineers often create web services and Application Programming Interfaces (APIs) to serve end-users with machine learning and artificial intelligence model results.
- Automation: Machine learning engineers automate model training and evaluation systems so that the artificial intelligence software can think on its own.
- Cleaning data: Data used for AI model training must first be cleaned to be readily available for the flow of data between machine learning models and an organization’s data systems.
- Improving systems: Machine learning engineers are responsible for developing and applying algorithms to improve systems by automating routine tasks that humans would otherwise do.
Not all machine learning engineers perform all of these roles. A job’s responsibilities often depend on the organization.
For example, a company may want its machine learning engineers to focus on “big picture” design while tasking a team of developers with implementing this vision.
On the other hand, a small organization may have a machine learning engineer involved in every step of this process.
Machine Learning Engineer Career Outlook and Salary
Machine learning engineering is a career path that is always in demand. As organizations become increasingly reliant on computers as part of their daily business, they need people to design, build, and maintain their software.
The Bureau of Labor Statistics (BLS) predicts a 23 percent growth in the field between 2022 and 2032, which is “much faster than average.”
The BLS does not specifically track machine learning engineers, but it does have information on computers and information research scientists.
A machine learning engineer’s profile is comparable to a computer and information research scientist’s. Regardless of title, each requires a master’s degree or higher in computer science or a related field to design innovative uses for new computing technology.
This is generally with a master’s degree and the median years of work experience required by current job listings, so candidates with a higher degree or greater experience can likely expect higher salaries.
Frequently Asked Questions
A Machine Learning Engineer applies mathematics and computer science principles to design and build AI systems based on predictive modeling. Their role is crucial in creating programs that enable machines and software to perform tasks autonomously.
To become a Machine Learning Engineer, one typically needs a master’s degree or occasionally a Ph.D. in artificial intelligence, machine learning, or data analytics. This advanced knowledge in mathematics and data analysis is essential in the digital age.
Employers usually look for evidence of experience with software development, along with specialized experience in machine learning and AI systems. Most Machine Learning Engineers have 3 to 10 years of experience and a background that includes internships, bootcamps, or hackathons.
Responsibilities include translating data science work into user-friendly applications, creating web services and APIs, automating model training and evaluation, cleaning data, and improving systems through algorithm development.