Are you interested in learning how to become a machine learning engineer?
A machine learning engineer job can pay as much as $190K+ (and even $250K in some cases) annually.
The demand for engineers is exploding with the rise of data science, artificial intelligence, and cloud technologies.
Hi, I’m Carol, a software engineer and instructor. I blog about online education to help people find the best classes to boost their careers. I aim to teach you how to use education to advance your career.
This article teaches you how to become a successful, in-demand machine learning engineer. You will learn about machine learning education, salary, and job roles.
TL;DR:
- Machine Learning Engineer jobs call for Bachelor’s, Master’s, or doctorate degrees but often accept relevant technical education and job experience instead of formal education.
- Google, Microsoft, Amazon, and IBM offer accredited certification paths for Machine Learning Engineers within their ecosystems.
- A Github project portfolio is an excellent way to show off your skills to job interviewers.
- Machine Learning jobs pay an average of $145K in the United States.
- ML Engineering requires a complex educational path; consider taking a few test courses before deciding on your new career path.
- ML combines programming, computer science, data analysis, and software engineering data constructs.
The Steps to Becoming a Machine Learning Engineer
To become a machine learning engineer, you will need:
The Degree You Need to Be a Machine Learning Engineer
The most pressing question in learning how to become a Machine Learning engineer is: “Do you need an advanced degree?”
You can become a machine learning engineer with only a high school diploma or an Associate’s Degree.
However, you’ll make more money and have a much larger pool of jobs with a Bachelor’s, Master’s, or Doctoral degree.
Bachelor’s and Master’s Degree candidates have the largest choice of employment.
On the day I gathered the job statistics for machine learning engineers:
- 2,570 machine learning job listings paying $60K to $125K required a high school diploma
- 3,579 machine learning job listings paying $50K to $125K required an Associate’s degree
- 28,706 machine learning job listings paying $90K to $175K required a Bachelor’s degree
- 47,981 machine learning job listings paying $90K to $175K required a Master’s degree
- 17,279 machine learning job listings paying $75K to $190K required a doctoral degree
By earning a Bachelor’s degree, gaining entry-level work experience, and potentially pursuing an advanced degree, you can increase your chances of securing a position as a machine learning engineer.
Continuous learning and staying updated with the latest advancements in the field are vital to staying relevant as a machine learning engineer.
The industry is constantly evolving, and adapting and enhancing your skills is essential.
- Develop strong programming skills and gain a solid understanding of the fundamentals of machine learning.
- Build a robust portfolio of industry-level machine learning projects and collaborate with other professionals in the field.
- Consider earning a Bachelor’s degree in computer science or a related field to increase your job prospects.
- Gain practical work experience through entry-level positions in machine learning engineering.
- Continuously update your skills and stay updated with the latest advancements in the field.
The Advanced Degree for a Machine Learning Engineer
The bias toward a college education is stronger in machine learning than in other technical fields.
If you don’t build a strong portfolio and create helpful relationships with colleagues, you at least want to earn a Bachelor’s degree in computer science or data science.
Concentrate on courses in mathematics, statistics, and data analysis.
Some universities offer specialized programs or concentrations in machine learning or artificial intelligence, giving you an edge in the job market.
The Online Course and Bootcamp Path to Machine Learning
While a degree is valuable, it is not the only path to becoming a machine learning engineer.
Many professionals in this field have successfully entered the industry through online courses, bootcamps, and self-study.
Online platforms like Coursera, Udacity, and edX offer comprehensive machine-learning courses taught by experts.
These courses provide practical hands-on experience and allow you to learn at your own pace, making them a flexible option for career changers or those looking to upskill.
Remember, the journey doesn’t end once you receive your degree or complete a course.
Machine learning is rapidly evolving, and staying up-to-date with the latest advancements is crucial.
Continuous learning through workshops, conferences, and online resources will help you remain competitive in the job market and enhance your skills as a machine learning engineer.
| Table: Pros and Cons of Machine Learning Educational Options | ||
| Education Path | Pros | Cons |
| Bachelor’s Degree |
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| Online Courses |
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| Self-Study |
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Developing Technical Skills for Machine Learning Engineering
In addition to formal education, developing specific technical skills is crucial for a successful career in machine learning engineering.
Developing technical skills is crucial for a successful career in machine learning engineering.
You can become a proficient machine learning engineer by mastering programming languages, machine learning algorithms, data analysis, and statistics.
Acquiring certifications from recognized organizations can boost your credibility and provide a competitive edge in the job market.
As a machine learning engineer, you will be responsible for designing, developing, and implementing machine learning algorithms and models.
It would be best to acquire expertise in several key areas to excel in this field.
Do You Have the Passion?
Machine learning is a complex topic requiring at least two years of education.
If you connect with the material, you will probably enjoy a career in machine learning.
But if you don’t connect with it?
This path is longer than the one to become a programmer.
If you don’t like machine learning statistics and math, consider a different direction.
As you embark on your educational journey to become a machine learning engineer, remember that dedication, persistence, and a genuine passion for the field will pave the way to success.
Whether you choose a traditional degree program or opt for alternative learning methods, continuous growth and a commitment to lifelong learning will be vital in thriving as a machine learning engineer.
Programming Skills
Proficiency in programming languages such as Python, R, and Java is essential for a machine learning engineer.
You must write clean, efficient, scalable code to develop and deploy machine learning models.
Machine Learning Algorithms
Understanding and implementing various machine learning algorithms, including supervised and unsupervised learning, is vital.
You should know algorithms like linear regression, decision trees, support vector machines, and neural networks.
Data Analysis and Statistics
An in-depth understanding of statistical concepts and data analysis techniques is essential for effectively working with large datasets.
Knowledge of statistical modeling, hypothesis testing, and data visualization will enable you to draw meaningful insights from the data.
| Skills | Examples |
| Programming | Python, R, Java |
| Machine Learning Algorithms | Supervised Learning, Unsupervised Learning, Linear Regression, Decision Trees, Support Vector Machines, Neural Networks |
| Data Analysis and Statistics | Statistical Modeling, Hypothesis Testing, Data Visualization |
| Data Structures and Algorithms | Arrays, Linked Lists, Graphs, Sorting Algorithms |
| Libraries and Frameworks | TensorFlow, Pytorchs, sci-kit-learn, Keras |
Data Structures and Algorithms
Good knowledge of data structures and algorithms is essential for efficient data manipulation and optimization of machine learning algorithms.
Understanding concepts like arrays, linked lists, graphs, and sorting algorithms will enhance your problem-solving abilities.
Familiarity with popular machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras is crucial.
These tools provide pre-built functions and modules for developing and training machine learning models.
Gaining Machine Learning Engineer Certification
Relevant certifications can significantly enhance your credibility and demonstrate your proficiency as a machine learning engineer.
Certifications from reputable organizations like Google, Microsoft, and IBM validate your skills and knowledge.
Some popular certifications include:
- Google Cloud Certified – Machine Learning Engineer
- Microsoft Certified: Azure AI Engineer Associate
- IBM Data Science Professional Certificate
- AWS Certified Machine Learning – Specialty
Earning these certifications showcases your commitment to professional growth and can open doors to exciting career opportunities.
Your Machine Learning Engineer Career Paths
You can work in diverse industries and roles as a machine learning engineer.
Specific domains include:
Just a few of the Machine Learning career choices include:
Machine Learning Engineer Industries
Machine learning engineers are in high demand across various industries, including technology, finance, healthcare, and e-commerce.
They are crucial in developing and implementing machine learning algorithms to solve complex problems, improve processes, and make data-driven decisions.
By leveraging their expertise in mathematics, statistics, and programming, machine learning engineers contribute to advancing AI technologies.
Machine Learning Engineer Career Paths
Regarding career paths, machine learning engineers can choose to specialize in specific domains such as natural language processing, computer vision, or predictive analytics.
Machine Learning Roles
You can work as a research scientist, software engineer, data scientist, or AI consultant.
Additionally, you can pursue leadership positions, becoming a machine learning team manager or a technical director.
Average Machine Learning Engineer Salary Data
According to industry reports, the average salary for a machine learning engineer is $145,000 per year.
However, salaries vary depending on experience, location, company size, and industry.
Machine learning engineers with advanced degrees or certifications may command higher salaries and have better career advancement opportunities.
Here’s a closer look at some popular career paths in machine learning engineering:
| Career Path | Job Description | Salary Range |
| Data Scientist | Apply machine learning algorithms to analyze and interpret complex data sets, derive insights, and drive data-driven decision-making. | $95,000 – $160,000 per year |
| AI Research Scientist | Provide strategic guidance to businesses, identify opportunities for applying machine learning, and develop custom ML solutions. | $100,000 – $190,000 per year |
| Software Engineer | Design, develop, and deploy software applications that utilize machine learning algorithms to solve specific problems or improve processes. | $85,000 – $150,000 per year |
| Machine Learning Consultant | Provide strategic guidance to businesses, identify opportunities where machine learning can be applied, and develop custom ML solutions. | $110,000 – $180,000 per year |
These are just a few examples of the career paths available to machine learning engineers.
The field continues to evolve, presenting new opportunities and challenges.
By staying updated with the latest advancements in machine learning and continuously honing their skills, machine learning engineers can thrive in this exciting field.
Building a Strong Machine Learning Portfolio
A well-crafted portfolio is a powerful tool for machine learning engineers.
Here’s how to build an impressive portfolio that showcases your skills and expertise.
Start with Personal Projects
Begin by working on personal machine-learning projects to demonstrate your abilities.
Choose a variety of projects that showcase different techniques and algorithms.
This will help you demonstrate your versatility as a machine learning engineer.
Collaborate with Professionals
Collaborating with other professionals in the field is a great way to enhance your portfolio.
Join online communities, participate in open-source projects, and collaborate on machine learning competitions.
This will expand your network, allow you to learn from others, and showcase your ability to work in a team.
Build Real-World Applications
When showcasing your projects, emphasize your work’s real-world applications and impact.
Describe how your models can be used to solve practical problems or improve existing systems.
This will demonstrate your understanding of the business implications of machine learning and make your portfolio stand out.
Show Off Your Technical Skills
Include code snippets, visualizations, and technical documentation in your portfolio.
This will give potential employers insight into your problem-solving skills and technical proficiency.
Explain your thought process and decision-making throughout each project.
For example:
“Through my collaborative project with XYZ company, we developed a machine learning model that reduced customer churn by 20%.
This saved the company millions of dollars in revenue and improved customer satisfaction.
I wrote the core algorithm for the model and worked closely with the data science team to fine-tune it for optimal performance.
The project was an excellent opportunity to apply my natural language processing and predictive modeling expertise.”
Create a GitHub Portfolio and Keep it Up-to-Date
Create real-world machine learning projects on GitHub to share with potential employers.
Regularly update your portfolio with new projects and achievements.
This will show that you are actively engaged in the field and constantly seeking opportunities to learn and grow.
Remember to include links to your GitHub profile or any relevant online platforms where your work can be accessed and reviewed.
By following these steps, you can create a compelling machine-learning portfolio that will impress potential employers and demonstrate your expertise in the field.
| Table: Build a Machine Learning Engineering Portfolio | |
| Key Elements of a Strong Machine Learning Portfolio | Examples |
| Project Descriptions | Detailed explanations of the problem, approach, and results of each project |
| Code Samples | Snippets of code to showcase your programming skills and problem-solving abilities |
| Visualizations | Graphs, charts, and interactive visualizations that illustrate the insights gained from your models |
| Technical Documentation | Clear explanations of the algorithms, techniques, and libraries used in your projects |
How New Machine Learning Candidates Gain Work Experience
While education and technical skills are essential, gaining real-world experience is crucial to thrive as a machine learning engineer.
Next, we will examine how you can gain valuable work experience in this field.
Internships and Co-Op Programs
One of the best ways to gain work experience in machine learning engineering is through internships or co-op programs.
These opportunities allow you to work on real projects alongside experienced professionals, giving you hands-on experience and the chance to apply your knowledge practically.
Many tech companies, research institutions, and startups offer internships specifically for machine learning engineers.
Contribute to Open Source
Another option is to contribute to open-source projects related to machine learning.
By actively participating in developing these projects, you can showcase your skills, collaborate with other professionals in the field, and gain recognition in the machine learning community.
This can be an excellent way to build your portfolio and demonstrate your ability to work on real-world machine-learning problems.
| Table: Sample Machine Learning Engineering Internships | |
| Company | Position |
| Machine Learning Engineer Intern | |
| Microsoft | Machine Learning Engineer Intern |
| Indeed.com | Machine Learning Engineer Internship Listings |
How to Network for Your Career
Additionally, networking plays a significant role in gaining work experience.
Attending industry conferences, workshops, and meetups allows you to connect with professionals and potential employers in the machine learning field.
Building relationships with industry experts can lead to valuable mentorship opportunities, job referrals, and even freelance projects that can contribute to your experience.
- Attend industry conferences
- Participate in industry workshops
- Organize or participate in industry meetups
Remember, gaining work experience in machine learning engineering is an ongoing process.
As you progress in your career, seek out challenging projects, continue learning and honing your skills, and stay updated with the latest developments in the field.
By combining education, technical skills, and real-world experience, you can position yourself for success as a machine learning engineer.
Large Companies Like Google Hire Machine Learning Engineers
I just found 30,555 Machine Learning jobs on Indeed.com.
A random sampling shows that the following companies are looking for candidates:
- Adobe
- GE Healthcare
- TikTok
- University of Washington
- Oracle
- Intel
- Microsoft
- Deloitte
- Uber
- IBM
- National Security Agency
- Proctor & Gamble
- Bloomberg
- Stanford University
Staying Relevant and Advancing Your Career Machine Learning Career
You will need to stay relevant in your field.
Machine Learning will change fast, so you will need continuing education.
Consider keeping up-to-date by:
- going to and participating in workshops
- attending conferences
- exploring online courses
A Master’s or Ph.D. in a relevant field like computer science or artificial intelligence can provide you with specialized knowledge and expertise to set you apart in the industry.
The market pays for a deeper understanding of algorithms, statistics, and tools.
This level of expertise will make you more competitive in the job market and open doors to more lucrative job opportunities and leadership roles within organizations.
In addition to the academic knowledge gained from an advanced degree, pursuing higher education allows you to network with industry professionals and researchers at the forefront of machine learning innovation.
Collaborating with experts in the field can expand your knowledge, expose you to cutting-edge research, and foster valuable connections that can propel your career forward.
Continuous Learning
Machine learning is a constantly evolving field.
Stay updated with the latest research papers, attend conferences, and join online communities to keep learning.
Explore online courses and tutorials to sharpen your skills and stay current with new algorithms, frameworks, and techniques.
Collaborate and Network
Connect with other professionals in the machine learning community.
Join forums and discussion groups, participate in open-source projects, and attend meetups.
Collaborating with others will help you learn from their experiences and provide opportunities for growth and collaboration on exciting new projects.
Building a Machine Learning Portfolio
Create a strong portfolio of machine learning projects to showcase your skills and expertise.
Include projects demonstrating your ability to solve real-world problems and various techniques and algorithms.
This will help you stand out to potential employers and clients.
Stay Informed
Keep up with industry news and advancements in machine learning.
Read blogs, follow influential figures in the field, and subscribe to newsletters to stay informed about the latest trends, tools, and technologies.
This will give you an edge regarding job interviews and staying ahead of the competition.
Machine learning is highly competitive, and staying relevant is crucial to long-term success.
You can be valuable in the ever-evolving machine-learning industry by staying current with the latest trends, continuously learning, collaborating with others, and building a solid portfolio.
| Resource | Description |
| Coursera | Online learning platform offering a wide range of courses on machine learning, data science, and artificial intelligence. |
| Kaggle | Online community for data science and machine learning enthusiasts. Participate in competitions and collaborate with other professionals. |
| ArXiv | Archive of research papers in various fields, including machine learning. Stay updated with the latest research and advancements. |
| Machine Learning Mastery | Website offering tutorials, books, and resources for machine learning beginners and professionals. |
Conclusion
Embarking on a career as a machine learning engineer can be a rewarding and lucrative choice in today’s technology-driven world.
By following the educational path, developing the necessary skills, and staying updated, you can position yourself for success in this exciting field.
- To become a machine learning engineer, it is crucial to start by developing strong programming skills and gaining a solid understanding of the fundamentals of machine learning.
- Acquiring knowledge of data analysis, statistics, data structures, algorithms, and libraries will lay a solid foundation for your career.
- Building a solid portfolio of industry-level machine learning projects and collaborating with other professionals in the field is also essential.
- This will showcase your capabilities and provide valuable networking opportunities and industry insights.
- While a degree is not always mandatory in this field, earning a bachelor’s degree in computer science can significantly enhance your chances of securing a machine learning engineering position.
- Entry-level work experience and potentially pursuing an advanced degree can further advance your career.
The machine learning industry has experienced significant growth and offers promising career opportunities, with a high demand for skilled professionals.
The average salary for a machine learning engineer is $145,000, making it a financially rewarding path to pursue.
Lastly, continuous learning and staying updated with the latest advancements are crucial to stay relevant in this field.
The machine learning landscape constantly evolves, and being adaptable and proactive in acquiring new knowledge and skills will ensure your long-term success as a machine learning engineer.
You can embark on an exciting and fulfilling career as a machine learning engineer by starting with the right education, gaining relevant experience, and staying dedicated to continuous learning.
