How to Become a
Machine Learning Engineer
Education Pathways, Skills, and Certifications
Machine learning is one of the most disruptive technologies in the tech industry, and the field still has room to grow. If you get started down a machine learning career path now, you’ll have plenty of opportunities to advance.
This article is for anyone who wants to learn how to become a machine learning engineer. It doesn’t matter whether you’re already a software developer or you’re just about to dive into the tech industry. For details about where aspiring machine learning engineers should go to get their education, skills, and certifications, read on.
What Is a Machine Learning Engineer?
A machine learning engineer is a computer programmer who creates algorithms that allow machines to take self-directed actions. One of the key factors separating us from machines is the human brain. A machine learning engineer studies how the brain works and adds elements of human thinking to computers.
Machine learning engineering is a subset of artificial intelligence that gives computers the ability to learn from existing data without explicit programming. A well-known application of machine learning technology is a self-driving car.
What Does a Machine Learning Engineer Do?
The job description of a machine learning engineer varies depending on the job title and organization, but they all create self-directed computer algorithms. Beyond this, the job entails combining the principles of computer science and data science. Below is a list of some tasks that most machine learning jobs have in common.
Design Machine Learning Systems
As a machine learning engineer, you will be charged with designing machine learning systems to match your organization’s needs. The type of system will depend on the organization.
In a pharmaceutical research company, for example, your designs will be used for pharmaceutical research. You may be required to collect data and write algorithms whose predictions can be used to create more effective medications.
Designing and Deploying Deep Learning Models
Machine learning engineers across industries also deploy deep learning models to solve problems in an organization. For this reason, you will need to learn deep learning as well as machine learning.
The application of deep learning techniques is broad. It increases productivity and efficiency while maximizing profit. Two examples of deep learning in action are virtual assistants like Apple’s Siri and Amazon’s Alexa, which are programmed to identify patterns and make decisions like a human brain.
Study and Transform Data Science Prototypes
Have you ever wondered why machine learning is taught in data science programs? Using machine learning algorithms, data scientists can efficiently observe, test, or transform prototypes. These prototypes can make high-value predictions without human intervention.
Firms like Facebook and Google rely on machine learning algorithms to analyze large volumes of data and offer users personalized experiences. They use machine learning algorithms for customized news feeds and ads.
Machine Learning Engineer Education Pathways
Now that you have an idea of what machine learning engineers do, it’s time to consider the paths you can follow to get your education. There are colleges, coding bootcamps, and individual courses that can help you land a machine learning engineer position.
Machine Learning Engineer Degrees
The most common degree for machine learning engineers is a Bachelor of Science (BSc). A Bachelor’s Degree in Computer Science or a related field would give you a headstart. Most computer science programs allow you to specialize in machine learning engineering. You can get the degree in about four years.
A graduate degree like a master’s or doctorate will give you an edge over someone who only has a bachelor’s degree. You will earn more money, and you will be eligible to apply for managerial roles.
Machine Learning Engineer Bootcamps
If you have a strong STEM background but don’t want to spend four years in college, you can apply for admission to a bootcamp that offers a comprehensive machine learning program. While bootcamps don’t offer degrees, they can make you job-ready in a few weeks or months, and they typically cost less than colleges.
Bootcamps teach machine learning as part of artificial intelligence or data science programs. Some of the best bootcamps for machine learning are Thinkful, BrainStation, Galvanize, and Flatiron School. These bootcamps also provide career services, so you won’t be on your own while looking for a job.
Machine Learning Engineer Courses
While most people who choose this option are already software engineers, literally anyone can take a massive open online course (MOOC) in machine learning. MOOCs usually have shorter timelines to completion than bootcamps and degree programs.
Coursera provides a standalone course called Machine Learning and also offers a four-course series on the topic. Edx also has a machine learning course, and Fast.ai offers an introduction to machine learning for coders. Remember that completing a course on machine learning doesn’t automatically make you a machine learning engineer.
How to Become a Machine Learning Engineer: A Step-by-Step Guide
The fastest way to dive into machine learning is to enroll in a reliable bootcamp. It should be one that has an excellent job placement rate (85 percent or higher), and it should offer one-on-one career coaching. In six months or less, you will be ready to take on a machine learning engineering role.
Attending a bootcamp might be the fastest way, but it’s not the only way to become a machine learning engineer, and it’s not a guarantee. Below is a step-by-step guide that might help boost your chances.
Learn the right programming languages
Learn the machine learning workflow
You can learn the machine learning workflow by studying the theories and frameworks. The workflow goes like this: importation, processing, visualization, modeling, and evaluation. At this stage, you should be able to work with machine learning algorithms and experimental datasets.
Take things up a notch with deep learning technologies
Deep learning is a crucial subset of machine learning, and the most advanced deep learning framework in the industry is TensorFlow. TensorFlow speeds up the learning process because it is an open-source library. Python programs use it for numerical computation.
Start analyzing big data
If you’ve gone through stages one through three, step four should be easy. As much as you can, gather, analyze and process large datasets. Two great tools for this are Hadoop and Spark. You should also learn to use relational databases like SQL, MySQL, and SQLite.
Seal the deal and start job hunting
At this point, you already have the skills to be a machine learning engineer. However, you need to convince employers of your worth. You can do this by getting a degree, certification, or attending a bootcamp. It will boost your resume, making it easier for employers to see your actual value.
Top Machine Learning Engineering Skills
It’s not enough to get a degree or certification for machine learning. You need to add as many relevant skills to your ML resume as possible if you want an employer to notice you. Proficiency in specific skills will make you an excellent machine learning engineer.
You will most likely come across this branch of computer science while acquiring your ML education. It deals with a network of computers as opposed to a single one. You will be handling large volumes of data throughout your career as an ML engineer, so knowing your way around distributed systems will help.
This skill is a must for all engineers, and machine learning experts are no different. Your employer will want to know if you can apply probability, linear algebra, and algorithms to machine learning. You should also be an expert in statistics, optimization, and multivariate calculus.
Natural Language Processing (NLP)
This is a branch of artificial intelligence that deals with teaching computers how to understand human language. Machine learning engineers with NLP skills can program machines to interpret and flexibly respond to human speech.
Machine Learning Engineering Salary and Job Outlook
Machine learning engineers are among the highest-paid experts in the tech industry. According to PayScale, the average salary for machine learning engineers in the United States is $112,639. It may vary based on location, job title, skills, and years of experience. Taking these factors into account, the overall range is between $76,000 and $154,000.
According to Indeed, machine learning engineering jobs grew by more than 344 percent between 2015 and 2018. The demand isn’t diminishing any time soon. In 2019, Indeed named machine learning engineering the best job in the United States tech industry.
Entry-Level Machine Learning Engineer Job Requirements
You will need a bachelor’s degree in computer science for many entry-level ML positions. Increasingly, however, employers are hiring bootcamp graduates for entry-level jobs. If you are self-taught, you may be able to win a job by demonstrating your skills. Entry-level ML engineers earn an average of $94,289 per year, according to PayScale.
How to Prepare for Your Machine Learning Engineer Job Interview
You can ace the interview with the right preparation. Irrespective of your educational background or experience, you will have to do well in the interview portion of the application process.
Below is a list of the most common questions to expect during an ML engineering job interview.
Machine Learning Engineering Job Interview Practice Questions
- With examples, what are the critical differences between supervised and unsupervised machine learning?
- What role does Bayes’ Theorem play in machine learning?
- What is the role of deep learning in machine learning, and why is it so important?
- Is machine learning model performance more important than machine learning model accuracy? Why did you choose your answer?
Machine Learning Engineer Certifications
There aren’t many recognized machine learning engineer certifications, but people who want to upskill may opt for an ML certificate program. There are several machine learning certificates you can get online or in-person. Universities like Harvard, MIT, and UC Berkeley offer certifications in collaboration with MOOCs.
Keep reading to find the best certificate programs for ML engineers.
Berkeley ExecEd - Artificial Intelligence: Business Strategies & Applications
This certificate is perfect for professionals looking for promotions and senior managers who want to improve their skills. It lasts for two months, and it covers the integration of AI and machine learning in existing business structures. The cost is $2,800. Upon completion, you will be awarded a Certificate of Completion from UC Berkeley Executive Education.
MIT Professional Education - Machine Learning: From Data to Decisions
If you’re a top-level executive who has a basic understanding of statistics and you want to upskill, this program is for you. It runs for about eight weeks and costs $2,300. You will learn how to improve neural networks, create decision-making apps, and you will develop a well-rounded understanding of machine learning.
Harvard University (edX) - Machine Learning Data Science Certification
This is an extensive program that runs for at least 36 weeks. It includes nine different machine learning courses, with each one lasting a minimum of four weeks. You can take the classes at your pace. The cost is $792.80 for all nine courses and a single certificate. However, you can also take individual classes without a certificate for free.
Simplilearn - Machine Learning Certification Course
Simplilearn offers self-paced and online certificate courses in machine learning. All classes will be based on real-world datasets, and you will learn how to apply different machine learning algorithms. Self-paced classes cost $699, while online, instructor-led classes cost $799. The timeline depends on the option you choose.
How Long Does It Take to Become a Machine Learning Engineer?
It takes about six months to four years, depending on the education path you choose. Those who opt for a degree program spend four or more years in school. They may also have to take specialized professional courses to improve their chances on the job market.
If you attend a bootcamp, you can become a machine learning engineer in a few weeks or months, depending on the duration of the bootcamp. If you take a self-paced learning approach, the timeline will depend on how many hours you spend learning weekly.
Why You Should Become a Machine Learning Engineer in 2021
Data from LinkedIn shows that machine learning was among the fastest-growing jobs in the United States in 2017. This is still the case, as there is still a significant skills gap between vacant ML positions and ML engineers. You should become a machine learning engineer in 2021 because it presents you with the perfect opportunity to thrive in the tech industry.
Machine Learning Engineering FAQ
Yes, you do. If you are a machine learning engineer, you are also a software engineer because machine learning is a specialized form of software engineering. You may also be called a data engineer or an artificial intelligence engineer.
Yes, you do. While machine learning goes beyond programming, learning programming languages like Python and R is essential to your career. You can start with these two and learn others along the way.
Yes, you do. A good combination of technical skills and soft skills will help you move up the career ladder quickly. Some non-technical skills you should develop are communication, teamwork, leadership, problem-solving, creativity, and multitasking.
Six to 10 years. It depends on several variables, including your education, professional milestones, and the company. Senior Machine Learning Engineers earn an average salary of $155,469, with a range of $112,000 to $184,000, according to PayScale. With benefits and bonuses factored in, the range is between $117,000 and $209,000.