machine learning engineer (ML engineer)

What is a machine learning engineer (ML engineer)?

A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building and designing self-running artificial intelligence (AI) systems to automate predictive models. Machine learning engineers design and create AI algorithms capable of learning and making predictions that define machine learning.

An ML engineer typically works as part of a larger data science team and communicates 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 they typically act as a bridge between data scientists who focus on statistical and model-building work and the construction of machine learning and AI systems.

Roles and responsibilities of a machine learning engineer

The machine learning engineer role assesses, analyzes and organizes large amounts of data, while executing tests and optimizing ML systems and algorithms.

An ML engineer's primary goals are creating machine learning models and retraining systems when needed. Responsibilities vary, depending on the organization, but some common duties for this role include the following:

• Designing ML systems.
• Researching and setting up ML algorithms and tools.
• Selecting appropriate data sets.
• Picking appropriate data representation methods.
• Identifying differences in data distribution that affect model performance.
• Verifying data quality.
• Transforming and converting data science prototypes.
• Performing statistical analysis.
• Running machine learning tests.
• Using results to improve models.
• Training and retraining systems when needed.
• Extending machine learning libraries.
• Developing ML apps according to client requirements.
• Staying up to date on new trends and technologies in ML and AI.

Skills and qualifications to become a machine learning engineer

To become an ML engineer, an individual should have the following skills and qualifications:

• Advanced math and statistics skills in subjects such as linear algebra, calculus and Bayesian statistics.
• An advanced degree in computer science, math, statistics or related fields.
• A master's degree in machine learning, neural networks, deep learning or related fields.
• Strong analytical, problem-solving, communication and teamwork skills.
• Software engineering skills.
• Experience in data science.
• Experience with coding and programming languages, including Python, Java, C++, C, R and JavaScript.
• Experience working with ML frameworks.
• Experience working with ML libraries and packages, such as the Natural Language Toolkit.
• An understanding of data structures, data modeling and software architecture.
• Knowledge of computer architecture.
• Knowledge and fluency in cloud platforms such as Amazon Web Services (AWS).

Certifications an ML engineer might need

ML engineering is an emerging field. As the demand for these specialists continues to grow, the field has become increasingly competitive. Pursuing certification courses and exams can help candidates showcase their abilities to potential employers, validate their expertise, and deepen their understanding of the technical concepts and tools needed to address real-world challenges.

The following are a few popular machine learning certifications that all current and prospective ML engineers should consider pursuing:

IBM Machine Learning Professional Certificate

IBM offers this online program on the Coursera platform. This certificate equips students with skills in machine learning algorithms, data science and Python programming. It covers topics such as data preprocessing, deep learning, model evaluation and deployment. Upon completion of the program's six courses, students receive a certificate from Coursera and a digital badge from IBM to demonstrate their machine learning expertise. This program is suitable for both individuals interested in pursuing careers as machine learning engineers and professionals seeking to enhance their knowledge and skills in the field. There are no prerequisites for this certification, which costs $49 per month for a subscription to Coursera.

AWS Certified Machine Learning -- Specialty certification

Compared with other certifications, this certification from Amazon is more focused. It aims to improve a person's ability to design, develop and create machine learning by using models with the AWS Cloud. To cater to a larger audience of data professionals, this program is offered in English, Korean, Japanese and Chinese. The certification exam has a score range of 100 to 1,000, and to pass the exam, an individual must score at least 750. The cost to take this exam is $300.

Google's Professional Machine Learning Engineer certification

This certification validates a candidate's expertise in designing, building and deploying ML models using Google Cloud and industry-proven techniques. To earn this certification, candidates must undergo and pass a two-hour exam with 50 to 60 multiple-choice questions covering topics such as problem framing, solution architecture and model development. The certification costs $200 plus taxes and is valid for two years, after which recertification is required.

Certificate in Machine Learning by Stanford

Stanford University offers an 11-week course covering essential topics in machine learning, including mathematics and statistics. This well-known program is acknowledged by machine learning experts and is available in 10 languages. Professors delve into core ML techniques and their practical applications in disciplines such as computer vision, audio manipulation, data mining and medicine throughout the course. A seven-day trial is offered for the course, which costs $49 per month thereafter.

Harvard Data Science: Machine Learning certificate

This course teaches students about various data science methodologies, including ML algorithms and cross-validation. In addition, students are guided in creating practical, real-world applications such as recommender systems and image classifiers. Users can choose between two options: free auditing or verified for $109.

Types of ML engineer titles

Job titles for machine learning engineers can differ across organizations and industries. Moreover, there can be some overlap with other data-related roles such as data scientist, data engineer and data analyst. However, it's important to note that these are distinct career paths with separate responsibilities.

Some common titles for ML engineers include the following:

• 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 engineer.

ML engineer salary and job demand

According to online training company 365 Data Science, the demand for AI and ML specialists is expected to grow by 40% from 2023 to 2027.

A machine learning engineer's compensation can vary depending on their experience, industry and location, among other things. According to pay aggregation websites, such as Payscale, ZipRecruiter, Salary.com and Glassdoor, machine learning engineers typically make between $112,832 and $143,180 per year in the U.S.

The average salaries of ML engineers based on their experience have been broken down by Payscale as follows:

• The average entry-level ML engineer salary is $93,867.
• The average junior-level ML engineer salary is $111,914.
• The average mid-level ML engineer salary is $141,720.
• The average senior ML engineer salary is between $147,630 and $150,322.

According to Indeed, the average ML engineer salaries based on several U.S. cities and states are as follows:

• Boston: $126,585.
• California: $119,732.
• Florida: $106,295.
• Los Angeles: $121,046.
• New York City: $127,759.
• San Francisco: $134,901.
• Seattle: $123,937.

Machine learning engineer vs. data scientist

Machine learning engineer and data scientist roles are similar, considering that both positions tend to include handling large amounts of data, require certain qualifications and use similar technologies. However, where ML engineers focus on creating and managing AI systems and predictive models, data scientists extract meaningful insights from large data sets.

A data scientist is 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 analytics technologies, including predictive modeling and machine learning techniques, as well as skills in mathematics, statistics, cluster analysis and visualization.

Other basic responsibilities of a data scientist include using various types of analytics and reporting tools to detect patterns, trends and relationships in data sets.

Machine learning engineers and data scientists work closely with each other and both require sufficient data management skills.

Big data plays a crucial role in training AI algorithms for enterprises, while AI, in turn, aids in comprehending and extracting insights from vast data sets. Learn how big data and AI collaborate to promote innovation and value for organizations.