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Machine Learning Engineer
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Written byBrennan Whitfield
UPDATED BY
Brennan Whitfield | Dec. 06, 2022
What Is a Machine Learning Engineer? How to Become One, Salary, Skills.
Machine learning engineers merge artificial intelligence (AI) technology and software engineering expertise to create machine learning mechanisms. Here’s what to know about a machine learning engineer’s salary, needed skills and how to become one.
What Is a Machine Learning Engineer?
Machine learning engineers are programming specialists who research, design and build machine learning systems for business use. The machine learning engineer role largely emerged in the 2010s due to the rise of big data and data science applications.
What Do Machine Learning Engineers Do?
Machine learning engineers utilize data sets to program and test predictive machine learning algorithms, models and schemas. These machine learning systems are often used by companies to automate tasks and forecast business decisions.
Machine Learning Engineer Responsibilities
- Design, test and build machine learning systems.
- Train and optimize machine learning algorithms and models.
- Research, convert and apply data prototypes for machine learning models.
- Conduct statistical analysis to gauge system performance.
- Add to and update machine learning frameworks and libraries.
Day-to-Day Responsibilities of Machine Learning Engineer
- Review existing projects and compare findings to working machine learning models.
- Use machine learning tools like TensorFlow to build and train models.
- Use programming languages like Python to code and debug algorithms.
- Access machine learning libraries like Apache Spark to load sample data and run linear regression models.
Machine Learning Engineer Within a Company
Machine learning engineers are usually part of a data science team within a company. They frequently collaborate with data analysts, data engineers, data scientists and software developers to accomplish their work.
Importance of Machine Learning Engineer
Machine learning engineers create systems often responsible for tracking product analytics and making impactful business decisions. Operations like content filtering and personalization, fraud detection, and voice recognition may not be as efficient or widely used without machine learning engineers.
What Skills Are Needed to Be a Machine Learning Engineer?
Qualifications to Be a Machine Learning Engineer
- Several years of experience in computer engineering, data science, software development or software engineering.
- Ability to build, train and deploy data structures, algorithms and models.
- Ability to assess and measure model performance.
- Proficiency in Python, R or Java as well as software engineering principles.
Machine Learning Engineer Prerequisites
- Bachelor’s degree in computer science, data science, mathematics, software engineering or a similar field.
- Master’s degree in computer science, data science, machine learning or a similar field.
Machine Learning Engineer Hard Skills
- Knowledge of big data tools and databases.
- Knowledge of cloud computing.
- Expertise in computer programming languages (C++, Java, JavaScript, Python, R).
- Experience with data analysis tools and techniques.
- Knowledge of deep learning and neural network algorithms.
- Familiarity with DevOps practices.
- Familiarity with MLOps practices.
- Experience with statistics, mathematics and related analysis.
Machine Learning Engineer Soft Skills
- Adaptability.
- Problem-solving skills.
- Time management skills.
- Verbal and written communication skills.
Tools and Programs Machine Learning Engineers Use
- Apache Hadoop
- Apache Mahout
- Apache Spark
- AWS Machine Learning
- C++
- Google Colaboratory
- IBM Watson Studio
- Java
- JavaScript
- Python
- PyTorch
- R
- TensorFlow
How to Become a Machine Learning Engineer
Machine Learning Engineer Education and Experience
Machine learning engineer candidates are often expected to have a bachelor’s degree in computer science, data science, mathematics or a similar field. They also may have a master’s degree in computer science, data science, machine learning or a similar field to enter competitive or higher-level roles.
Candidates often obtain several years of experience in computer engineering, data science, software development or software engineering before entering the machine learning engineer role. Knowledge in the areas of machine learning structures and models, data analysis tools, computer programming languages (C++, Java, JavaScript, Python, R), big data tools, cloud computing and effective communication are also recommended.
Machine Learning Engineer Certificates and Courses
- 20 Machine Learning Bootcamps and Classes to Know
- Become a Machine Learning Engineer for Microsoft Azure
- Data Science and Machine Learning Bootcamp with R
- Machine Learning A-Z™: Python & R in Data Science
- Python for Data Science and Machine Learning Bootcamp
Machine Learning Engineer Career Path
Prior to the machine learning engineer role, professionals may start their career as a computer engineer, software developer, software engineer or a similar role. After experience as a machine learning engineer, professionals can progress into management and leadership positions like senior machine learning engineer, lead machine learning engineer or director of machine learning engineering.
Machine Learning Engineer Salary and Job Outlook
Jobs for computer and information research scientists, including AI and machine learning specialists, are projected to increase 21 percent by 2031 according to the U.S. Bureau of Labor Statistics.
The full compensation package for a machine learning engineer depends on a variety of factors, including but not limited to the candidate’s experience and geographic location. See below for detailed information on the average machine learning engineer salary.
How do you become a machine learning engineer?
Machine learning engineers often possess a bachelor’s, master’s and two to four years of relevant work experience.
Because of their specialized focus, machine learning engineers must display a thorough education. Many students choose to major in computer science, engineering, data science or another related field. After completing their undergraduate careers, students can secure their master’s in data science, computer science or a similar concentration. There are also machine learning boot camps and classes that provide further credentials.
Machine learning may be a niche field, but professionals travel various career pathways to reach this specific sector. Machine learning engineers often spend two to four years working in entry-level positions like data analyst, software engineer and software developer. Acquiring prior experience provides a more balanced background that enables candidates to meet the software engineering and data science aspects of machine learning engineer positions.
What is the salary of a machine learning engineer?
Machine learning engineers in the U.S. can look forward to an average base salary of $145,297.
The growing demand for the specialized knowledge of machine learning engineers has resulted in hefty six-figure salaries. According to Built In’s salary tool, machine learning engineers in the U.S. earn an average base salary of $145,297. An additional cash compensation of $24,096 lifts the total compensation to $169,393. Machine learning engineers can supplement their expertise with proven experience through the years, going from salaries as low as $74K to salaries as high as $220K.
What skills does a machine learning engineer need?
Software and programming expertise, data analysis and research are core components of machine learning engineer positions.
Machine learning engineers need to display backgrounds in software engineering and data science to stand out in this in-demand field. Professionals should feel comfortable developing software with languages like Python, Java, C, C++, JavaScript, R, Scala and Julia. These positions also require engineers to handle large amounts of data and leverage this information to accelerate the automation of algorithms. As a result, the strongest candidates will know how to pivot between software engineering and data science.
Research also remains a major part of machine learning engineers’ daily routines. No algorithm or predictive model is perfect, so engineers must know how to conduct thorough testing and continually improve their models. Once machine learning engineers master these technical abilities, they should be able to blend these areas with a collaborative mindset and other soft skills needed for the workplace.
Courses
Expand Your Machine Learning Engineer Career Opportunities
Open more doors with skills acquired through Udemy’s online development and engineering courses.
Certifications
Machine Learning Engineer Certifications + Programs
Take your ambitions to new heights with Udacity’s online development and engineering certifications.
Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation, and more!
Udacity
Advanced
4 months
10 hours
In this program, students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom.
Udacity
Advanced
4 months
10 hours
In this program, you will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring.
Udacity
Advanced
4 months
10 hours
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