What is a machine learning engineer? An ML engineer is a professional in the field of information technology who specializes in developing self-contained artificial intelligence (AI) systems that automate the usage of prediction models. The AI algorithms capable of learning and making predictions are designed and built by machine learning engineers (ML).

Although being a machine learning engineer is not an entry-level position in the IT sector, the journey can be exciting and rewarding. Do you want to work as a machine learning engineer but are unsure where to start? You came to the right place to begin.

What is a machine learning engineer?

Machine learning engineers research, develop, and design self-running software to automate prediction models. An artificial intelligence (AI) engineer specializing in machine learning (ML) builds AI systems that employ enormous data sets to design and build algorithms that learn and make predictions.

ML engineers act as a bridge between AI systems and data scientists. An ML engineer generally collaborates with data scientists, administrators, data analysts, data engineers, and data architects as part of a wider data science team. Depending on the firm’s scale, they might interact with groups outside their teams, such as the IT, software development, sales, or web development teams.

The Machine Learning Engineer must evaluate, organize, and analyze data, run tests, and improve the learning process to design machine learning systems that produce high-performance machine learning models. Do you want to learn what exactly they do, keep reading, we have explained everything that you need to know about what is a machine learning engineer.

Check out the history of machine learning

What does a machine learning engineer do?

Machine learning engineers make it possible for machines to learn without additional programming by integrating software engineering with data analysis. Even scaling predictive models to fit better the volume of data that matters to the business is assisted by them.

ML engineers have some crucial obligations, and these make them substantial.

Check out the real-life examples of machine learning

Machine learning roles and responsibilities

What exactly does a machine learning engineer do? Let’s take a closer look at the machine learning roles and responsibilities they need to handle day-to-day.

• Machine learning system design and development.
• Implementation of ML & AI algorithms.
• Choosing suitable data sets.
• Data representation (Data visualization).
• Conducting statistical analysis.
• Designing deep learning frameworks for use in case-based situations.
• Deciding the appropriate way to prepare the data for analysis after analyzing big datasets.
• Build efficient data pipelines in collaboration with other data scientists.
• Affirming data quality.
• Work with necessary software libraries and common ML methods.
• Optimizing ML models.
• Explaining an ML model’s capabilities to key users and important stakeholders.
• Assisting relevant parties in using and comprehending machine learning systems and datasets.
• Developing machine learning apps.
• Enriching the libraries for machine learning.

Such responsibilities and roles call for a wide range of skills, right?

Machine learning engineer skills

What skills are needed for machine learning? What language do machine learning engineers use? Does machine learning require coding, or is there a lot of math in machine learning? We have all the answers; these are the most sought machine learning engineer skills:

• Applied mathematics.
• Creative problem-solving.
• Programming languages (Java, C, C++).
• Linux/Unix knowledge.
• Data intuition.
• Data modeling and evaluation.
• Neural Networks
• Natural Language Processing.
• Communication skills.

Is it too much? Believe us, it is not that complicated, and your potential salary is highly motivating to get these skills.

Machine learning engineer salary

What is the salary of a machine learning engineer? In the USA, it is approximately $113K. What about the whole world? These are average machine learning engineer salaries around the world, according to Indeed:

The wide range of a machine learning engineer’s typical income is due to several factors. You can be an entry-level or working in a different company. We have already gathered all aspects of it. Check out the article and learn everything you need to know about machine learning engineer salaries, including comparisons between data scientists and software engineers’ salaries. If you want to learn all the differences between these jobs, keep reading.

Comparison: Machine learning engineer vs data scientist

What is the difference between a data scientist and a machine learning engineer?

Check out the machine learning vs data science comparison and learn all the differences

Comparison: Machine learning engineer vs software engineer

What is the difference between a software engineer and a machine learning engineer?

Comparison: Machine learning engineer vs data engineer

What is the difference between a data engineer and a machine learning engineer?

Conclusion

Machine learning engineers play a crucial role in the data science team. In addition to maintaining and enhancing current artificial intelligence systems, their jobs include investigating, developing, and designing the artificial intelligence that powers machine learning.

After data architect, cloud computing, and data engineer jobs, machine learning engineers are hot and on the rise.

Machine learning engineering aims to apply software engineering and data science methods to turn machine learning models into usable functions for products and consumers. Artificial intelligence technology is created using machine learning engineering with massive data sets. Machine learning engineering develops AI systems and algorithms to learn and ultimately make predictions.

What is a machine learning engineer?

Machine learning engineers are competent software developers who research, design, and implement autonomous programs to create predictive models. Engineers must evaluate, analyze, and organize data, execute experiments, and optimize the training procedure to construct high-performance machine learning models. An ML engineer usually works within a large data science team and collaborates with data scientists, administrators, analysts, engineers, and architects.

What are the responsibilities of a machine learning engineer?

The objective of a machine learning engineer is to design machine learning models and retrain systems as needed. Their responsibilities vary according to the organization, but there are a few universal duties for this position.

Machine learning engineers design, develop, and study machine learning systems, models, and schematics. Examines and transforms data science prototypes. Seeks out and picks suitable datasets. They use statistical analysis to improve models and visualize data to gain deeper insights. Engineers also analyze the use cases of machine learning algorithms and rank them according to their probability of success.

Machine learning engineer salary and demand

AI projects fail because organizations lack technical knowledge, processes, tools, and know-how in deploying ML models. This challenge keeps the interest in machine learning engineering alive in many industries. In 2019, Indeed ranked machine learning engineer as the No. 1 job in the United States. The average base salary for an ML engineer in the US as of 2021 is $149,801, according to Indeed. According to Glassdoor, it’s lower at $127,326. However, salaries for machine learning engineers in well-known Silicon Valley companies ranging from $200,000 to over $250,000.

Machine learning engineering is not a career limited to tech-focused businesses. Despite the fact that it is a relatively new field, many organizations have already found success in applying machine learning to solve their problems. Machine learning expertise may be used by virtually any type of organization working with large amounts of data. Machine learning engineering is enabling businesses to get real-time insights from data and find ways to work more efficiently, which helps them gain a competitive advantage.

Over the past four years, the number of data science positions has increased by almost 75 percent and is projected to grow. Pursuing a career in machine learning is an excellent decision since it’s a high-paying profession that will be in great demand for years. Healthcare, education, marketing, retail and e-commerce, and financial services are among the industries that have already heavily invested in AI and machine learning.

How to become a machine learning engineer?

You must first acquire the necessary education and experience to become a machine learning engineer. Math, data science, computer science, computer programming, statistics, or physics are all acceptable bachelor’s degrees for machine learning engineering.

It’s unlikely that you’ll get your foot in the door as a machine learning engineer. You may need to choose a starting point such as software engineers, software programmers, data scientists, and computer scientists.

The majority of machine learning engineering jobs require more than an undergraduate degree. Seek a master’s or Ph.D. in data science, computer science, software engineering, or even a doctorate in machine learning to get one step closer to your dream job. Building a career as a machine learning engineer entails never-ending education. As technology advances, staying top on AI and cutting-edge technologies become more crucial. Understanding data structures, modeling, and software architecture is a must for this job.

What is the difference between machine learning engineering and a data scientist?

The primary distinction between a data scientist and a machine learning engineer is that the former focuses primarily on research, whereas the latter focuses on development. The two jobs have similar responsibilities in handling large amounts of data and necessitating specific qualifications, with both requiring comparable methods.

ML specialists focus on developing and managing AI systems and predictive models, while data scientists extract important discoveries from large data sets.

Data scientists are in charge of collecting, analyzing, and interpreting massive amounts of data. The data collected is used to construct hypotheses, draw conclusions, and analyze trends. The data scientists use complex analytics tools such as predictive modeling and machine learning procedures, mathematics, statistics, cluster analysis, and visualization abilities. Data scientists and machine learning engineers usually collaborate closely, and both need competent data management skills.