What is a Machine Learning Engineer (ML Engineer)?
A machine learning engineer is one of them who focuses on researching, building and designing self-executing artificial intelligence systems to automate predictive models. ML engineers design and create AI algorithms that can learn and create predictions that define machine learning.
ML engineers usually act as part of a large data science team and communicate with data scientists, deep learning engineers, administrators, data analysts, data engineers and data architects.
ML engineers have a solid foundation in computer science, mathematics and statistics, and typically serve as a bridge between statistical and model building tasks and data scientists focusing on building machine learning and AI systems.
Roles and responsibilities of machine learning engineers
The role of machine learning engineers is to run tests and evaluate, analyze and organize large amounts of data while optimizing ML systems and algorithms.
The main goal of an ML engineer is to create machine learning models and retraining systems as needed. Responsibilities vary from organization to organization, but some general duties in this role include:
- Design of ML systems.
- Investigating and setting up ML algorithms and tools.
- Selecting the right dataset.
- Choose the appropriate method of representation of data.
- Identify differences in data distribution that affect model performance.
- Checking data quality.
- Conversion and conversion of data science prototypes.
- Performing statistical analysis.
- Running machine learning tests.
- Use the results to improve the model.
- Training and retraining systems as needed.
- Expand the machine learning library.
- Develop ML apps according to client requirements.
- Stay up to date with new trends and technologies in ML and AI.
Skills and qualifications required to become an ML engineer
To become an ML engineer, an individual must have the following skills and qualifications:
- Advanced mathematics and statistical skills of subjects, such as linear algebra, calculus, and Bayesian statistics.
- Advanced degrees in computer science, mathematics, statistics, or related fields.
- Master's degree in machine learning, neural networks, deep learning or related fields.
- Powerful analysis, problem solving, communication and teamwork skills.
- Software engineering skills.
- Data science experience.
- Experience in coding and programming languages such as Python, Java, C++, C, R, JavaScript.
- Experience using the ML framework.
- He has experience working with ML libraries and packages such as the Natural Language Toolkit.
- Understanding data structures, data modeling, and software architecture.
- Knowledge of computer architecture.
- Knowledge and flowability of cloud platforms such as Amazon Web Services (AWS).
Certifications required by ML engineers
ML engineering is an emerging field. As the demand for these professionals continues to grow, the sector is becoming more and more competitive. Pursuing certification courses and exams will help candidates to introduce their abilities to potential employers, validate their expertise, and deepen their understanding of the technical concepts and tools needed to address real challenges.
Below are some popular machine learning certifications that current and future ML engineers should consider all of their pursuits:
IBM Machine Learning Professional Certificate
IBM offers this online program on the Coursera platform. This certificate equips students with machine learning algorithms, data science and Python programming skills. It covers topics such as data preprocessing, deep learning, model evaluation, and deployment. Once the six courses of the program are completed, students will receive certificates from Coursera and digital badges from IBM, demonstrating their machine learning expertise. This program is suitable for individuals interested in pursuing a career as a machine learning engineer, and for professionals looking to improve their knowledge and skills in this field. There are no prerequisites for this certification. This certification is $49 per month for Coursera subscriptions.
AWS Certified Machine Learning – Specialized Certification
Compared to other certifications, this certification from Amazon is more focused. It aims to improve the ability of people to design, develop and create machine learning using models with AWS cloud. To accommodate a larger audience of data experts, the program is offered in English, Korean, Japanese and Chinese. The score range for the certification exam is 100-1,000, and individuals must earn at least 750 to pass the exam. The cost to take this exam is $300.
Google's Professional Machine Learning Engineer Certification
This certification validates candidates' expertise in designing, building and deploying ML models using Google Cloud and industry-proven techniques. To win this certification, candidates must take and pass a two-hour exam with 50-60 multiple-choice questions covering topics such as problem framing, solution architecture, and model development. Certification costs $200 and taxes, and is valid for two years, and then requires recertification.
Harvard Data Science: Machine Learning Certificate
This course teaches students about a variety of data science methodologies, including ML algorithms and cross-validation. Additionally, students are guided to creating practical, real-world applications such as recommendation systems and image classifiers. Users have two options to choose from: You will be checked for a free audit or $149.
Types of ML Engineer Titles
Machine Learning Engineer job titles may vary across organizations and industries. Additionally, it may overlap with other data-related roles such as data scientists, data engineers, and data analysts. However, it is important to note that these are clear career paths with individual responsibility.
Common titles for ML Engineers include:
- Machine learning research scientist.
- ML developer.
- Junior machine learning engineer.
- Senior machine learning engineer.
- Machine learning software engineer.
- Algorithm engineer.
- Deep learning engineer.
- AI/ML engineer.
- Natural Language Processing (NLP) Engineer.
ML Engineer Salaries and Employment Demand
According to online training company 365 Data Science, demand for AI and ML professionals is expected to increase by 40% from 2023 to 2027.
The rewards of machine learning engineers vary depending on experience, industry, location, etc. Machine learning engineers typically make between $112,832 and $143,180 a year in the US, according to paid aggregation websites such as Payscale, Ziprecruiter, Salary.com and Glassdoor.
The average salary for ML engineers based on experience is classified by Coursera as follows:
- After 0-1 year of experience, the average ML engineer salary is $127,350.
- After 4-6 years of experience, the average ML engineer salary is $150,193.
- After 10-14 years of experience, the average ML engineer salary is $162,356.
- After 15 years or more experience, the average ML engineer salary is $170,603.
Certainly, the average ML engineer salaries based on some US cities and states are:
- Florida: $161,578.
- Boston: $163,799.
- Seattle: $172,525.
- California: $176,022.
- Los Angeles: $176,673.
- San Francisco: $182,854.
- New York City: $188,926.
Machine Learning Engineers vs Data Scientist
Given that both positions involve processing large amounts of data, require specific qualifications and tend to use similar technologies, the roles of machine learning engineers and data scientists are similar. However, when ML engineers focus on creating and managing AI systems and predictive models, data scientists extract meaningful insights from large data sets.
Data scientists are responsible for collecting, analyzing and interpreting extremely large amounts of data. This data is used to develop hypotheses and inferences and to analyze customer or market trends. This position requires the use of advanced analytical techniques, such as predictive modeling and machine learning techniques, such as mathematics, statistics, cluster analysis, and visualization skills.
Other fundamental responsibilities of data scientists include using different types of analysis and reporting tools to detect patterns, trends, and relationships in a dataset.
Machine learning engineers and data scientists work closely with each other, both of which require adequate data management skills.
The future of machine learning engineering
Market research firm Fortune Business Insights shows that the machine learning market is expected to be in an extreme period of growth. The company predicts that the global machine learning market will grow from $260.3 billion in 2023 to $2259.1 billion by 2030.
Current focus areas of machine learning engineering include technologies such as NLP, computer vision, machine learning in healthcare, and AI-assisted software development.
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Editor's Note: This article was updated in June 2024 to improve the experience of our readers, provide up-to-date information on machine learning certifications and salaries, and market size.
Kinza Yasar is a technical writer at Whatis with a degree in computer networking.
