With a triple-digit growth rate over the past five years and an average salary topping $120,000, machine learning engineer has quickly become one of the most sought-after IT careers. As organizations continue to find ways to leverage the power of artificial intelligence (AI) and machine learning (ML) to improve operations, the demand for ML engineers will continue to skyrocket.
In this guide, we'll outline the steps to become a successful machine learning engineer, the skills and education required, how much ML engineers can earn, and the job outlook for this exciting field. We’ll also highlight some of the best training programs in machine learning and related disciplines.
a.k.a. Deep Learning Engineer | ML Research Scientist | ML Software Engineer | Algorithm Engineer
What is Machine Learning?
Simply put, machine learning is a type of artificial intelligence that allows machines to automatically learn and improve from experience without additional programming. ML involves the use of algorithms and statistical models to analyze and identify patterns in data, enabling machines to make decisions based on new inputs.
What Does a Machine Learning Engineer Do?
Machine learning engineers design, build and deploy machine learning models. These models can recognize patterns in data, develop predictions, and make decisions based on that analysis. ML engineers work closely with data scientists and software engineers to integrate machine learning models into products and services.
How to Become a Machine Learning Engineer
Starting a career as a machine learning engineer requires a combination of education, hands-on training and experience. Here are the steps you can take to become a successful ML engineer:
Step 1: Get a Bachelor's in AI or a Related Field
A bachelor's degree in artificial intelligence or computer science is the first step towards becoming a machine learning engineer. This degree will provide you with the fundamental knowledge and skills required to work in AI, ML, and related fields. Make sure to choose a bachelor’s program that includes the ML engineering skills highlighted below.
Step 2: Learn Marketable ML Programming Languages
As a machine learning engineer, you will need to be proficient in programming languages like Python, R, Java, C++ and SQL. These languages can be used to build machine learning models and work with large datasets.
Step 3: Gain Experience in Data Science
Data science experience is essential for becoming a machine learning engineer. You can gain experience in data science by working on personal projects, participating in hackathons, or taking online data science courses.
Step 4: Earn a Master's Degree in ML or a Related Field
A master's program in artificial intelligence is not always required to become an ML engineer, but it can help you land a better position. Completing a targeted graduate degree in machine learning will provide you with advanced expertise and help you stand apart from the competition.
Step 5: Apply for Machine Learning Engineering Jobs
Once you have the requisite education, training, and experience, you can start applying for machine learning jobs. These positions are typically found at tech companies and require a strong understanding of machine learning, data science, and software development.
ML Engineer Skills and Qualifications
To achieve success as a machine learning engineer, you will need a combination of technical and soft skills. Here are some of the most important skill sets for machine learning engineers:
Computer Programming
ML engineers need coding skills in languages such as Python, R, Java, SQL, and C++, which are used to design and implement machine learning models.
ML Libraries and Frameworks
Knowledge of popular tools and frameworks, such as TensorFlow, PyTorch, Scikit-Learn, Theano, and Keras, is essential for training and developing machine learning models.
Data Analysis and Preprocessing
The ability to perform data analysis and preprocessing tasks like data cleaning, feature engineering and data visualization is crucial for handling large datasets and preparing them for machine learning.
Probability and Statistics
An understanding of probability and statistics is necessary for implementing various machine learning algorithms, evaluating their performance, and interpreting the results.
Deep Learning
Skills in deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are essential for solving complex machine learning problems, such as image and speech recognition.
Cloud Computing
Familiarity with cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) is increasingly important, as they provide scalable infrastructure for deploying and managing machine learning models.
Software Engineering
Proficiency in software engineering skills, such as version control, code testing and debugging, is crucial for developing and maintaining reliable and scalable machine learning systems.
Soft Skills for Machine Learning
Desirable soft skills for machine learning engineers include creative problem-solving, effective communication, collaboration, attention to detail, and the ability to work under pressure and meet deadlines.
Machine Learning Engineer Salaries
The average annual salary for machine learning engineers is $121,788. Here are some other lucrative career paths in machine learning and how much you can earn in each:
| ML Engineering Job | Average Salary |
|---|---|
| Machine learning engineer | $121,788 |
| Deep learning engineer | $146,231 |
| Computational linguist | $108,854 |
| Big data engineer | $126,274 |
| Artificial intelligence engineer | $173,985 |
| Principal Data scientist | $172,055 |
| Algorithm engineer | $127,968 |
The highest paying locale for machine learning engineers is the San Francisco Bay Area, which comes in at a whopping $209,890. Here are the top-paying cities and metropolitan areas for machine learning engineers:
| City / Metro Area | Hourly Wage | Annual Salary |
|---|---|---|
| San Francisco - Oakland - Hayward, CA | $100.91 | $209,890 |
| Sacramento - Roseville - Arden-Arcade, CA | $85.69 | $178,230 |
| Portland - Vancouver - Hillsboro, OR-WA | $81.34 | $169,180 |
| Dallas - Fort Worth - Arlington, TX | $72.91 | $151,650 |
| Riverside - San Bernardino - Ontario, CA | $71.73 | $149,190 |
| Houston - The Woodlands - Sugar Land, TX | $69.90 | $145,380 |
| Austin - Round Rock, TX | $69.82 | $145,220 |
Sources: Salary.com and U.S. Bureau of Labor Statistics
Machine Learning Engineer Training
Browse online courses, certificates and degrees in machine learning and related subjects.
Job Outlook for Machine Learning Engineers
The job outlook for machine learning engineers is excellent, with strong demand and top-tier salaries. The U.S. Bureau of Labor Statistics projects the job market for computer and information research scientists (including ML engineers) to grow by 21% from 2021 to 2031, dwarfing the 5% average growth rate for all occupations. The actual demand for machine learning engineers will be even higher, as more companies discover how to leverage the power of machine learning in coming years.
Source: U.S. Bureau of Labor Statistics
Machine Learning Engineer Jobs
Browse and apply to job openings in ML engineering and related fields:
- Machine learning engineer jobs
- Deep learning engineer jobs
- Artificial intelligence engineer jobs
- Data science jobs
- Algorithm engineer jobs
- Computer scientist jobs
