Machine Learning Engineer

Machine learning is an in-demand career path in data science and artificial intelligence. Organizations are adopting machine learning applications such as virtual assistants, product recommendations, speech recognition, and image recognition. The rise of big data also makes machine learning an integral part of digitization.

As a machine learning engineer, you will research, design, and program machine learning systems for your clients. You are also responsible for maintaining and ensuring the systems work smoothly. Machine learning engineering careers are extensive and require advanced math, statistics, and analytical skills.

Pursuing a career in machine learning (ML) comes with several benefits listed below.

  • Machine learning gives you access to a wide range of career paths. You can become a data scientist, artificial intelligence engineer, business intelligence developer, or even software engineer. ML engineers have the skills to venture into any of these career paths.
  • Machine learning professionals are in high demand. According to Payscale, a machine learning engineer in the United States earns an average salary of $112,850 per year. Furthermore, since machine learning is still an evolving career, there is a chance to make more in the future.
  • Machine learning is a relatively new field for most professionals. Therefore, you have an opportunity to learn and grow as the career path continues to evolve. You will explore new ML solutions, algorithms, and applications, making you an invaluable team player.
  • Machine learning careers allow you to implement your algebra, calculus, and statistics skills in real-life situations. If you enjoy solving complex theories and math equations, you should enter this field.
What Is Machine Learning

What Is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) that focuses on designing self-running computer systems. The aim is to create computer systems that function as humans and learn on their own.

The systems use present and past data along with machine learning algorithms to operate without human intervention. Over time, the computer systems adapt to these changes and provide accurate results. Machines can detect and filter spam messages, process images, run chatbots, and more.

What Do Machine Learning Engineers Do?

Machine learning engineers feed data into models developed by a data scientist, enabling the model to learn without human intervention. They fill many roles like researching, programming, and collaborating. You will play a significant role in state-of-the-art software development using an array of machine learning techniques.

In short, a machine learning engineer acts as a bridge between software engineering and data science. Below is a comprehensive list of a machine learning engineer’s job responsibilities.

Machine Learning Engineer Job Description

Organizations look for candidates who will lead the optimization of machine learning systems. You will analyze existing systems, improve datasets by performing statistical analysis, and work with colleagues to enhance the system’s accuracy.

Your day-to-day tasks will include the responsibilities below.

  • Designing machine learning systems and incorporating AI software to enable automation
  • Analyzing and converting data science prototypes by applying essential machine learning algorithms and features
  • Consulting managers and department heads to determine appropriate machine learning objectives
  • Collaborating with UX designers to design and develop efficient ML systems and schemes
  • Developing ML algorithms for big data analysis and predictions
  • Keeping records of all machine learning processes
  • Researching ML trends to develop current ML algorithms
  • Conducting text-to-speech conversions that convert unstructured data into useful information
  • Testing systems, performing statistical analysis, and translating test results

What Are the Required Skills for Machine Learning Careers?

Machine learning engineers need coding skills, math skills, and communication skills. This role entails plenty of responsibilities. You are in charge of crucial systems that enable the organization’s performance.

As you learn how to become a machine learning engineer, the skills you need to acquire are listed below.


Machine learning engineers, data scientists, and software engineers all rely on coding languages. Code allows these professionals to exchange data, algorithms, and problem-solving tools. Therefore, you need to have experience working with one or several coding languages. Popular coding languages include Python and JavaScript.

Data Modeling

Data modeling involves training machine learning algorithms to automate predictive models. You need data modeling skills to help you understand the data’s structure. There are several types of algorithms, including regression, classification, and clustering. You will select an algorithm for data modeling depending on your data.

Applied Mathematics

Machine learning consists of four pillars that require mathematical skills. They include statistics, probability, calculus, and linear algebra. Each pillar uses mathematics to solve problems and draw conclusions. Calculus requires you to calculate rates and changes in quantities, while statistics requires you to analyze and interpret numerical data.

Computer Science

As a machine learning engineer, you need to know computer science fundamentals. You will spend time operating computer systems and software. Therefore, you should have the skills to solve problems and develop ML algorithms with ease. Some necessary skills include data structures, programming, and computer organization.


Machine learning engineers work with software developers, other engineers, and managers. Therefore, employers look for candidates with excellent interpersonal skills and the ability to collaborate with others effectively. You need to hone your communication skills and be a team player to succeed in this role.

How Much Do Machine Learning Engineers Make

How Much Do Machine
Learning Engineers Make?

According to PayScale, machine learning engineers make an average of $112,850 per year. An entry-level machine learning engineer takes home an average of $93,339 per year.

However, salaries also differ depending on your location. Below is a table highlighting annual machine learning engineer salaries in various states across the country. The salary estimates originate from PayScale and are subject to change.

City Average Salary
San Francisco, CA $133,243
New York, NY $121,617
Seattle, WA $120,687
Portland, OR $104,965
Dallas, TX $103,478
Boston, MA $102,835
Atlanta, GA $101,034
Los Angeles, CA $100,000
Miami, FL $96,978
Minneapolis, MN $96,650
Chicago, IL $91,223
Philadelphia, PA $90,000

Apart from location, a machine learning engineer’s salary will also depend on career experience. An entry-level engineer doesn’t earn the same salary as an experienced machine learning engineer. Below is a table specifying salary projections according to experience.

Position Salary
Entry-Level (0-5 Years) $93,339
Mid-Career (5-10 Years) $137,738
Experienced (10-20 Years) $150,360
Late-Career (20+ Years) $162,000

Machine Learning Technologies and
Coding Languages


In recent years, Python has become one of the best coding languages. It is also the most preferred language for machine learning because of its consistency and simplicity. It offers readable codes, which makes work easier for engineers. Even an amateur can use Python seamlessly.

Machine learning engineers can rely on Python to write unique systems that humans can understand. If you’re stuck, you can ask for help from the strong community of Python users. Python also features a vast library of ready-to-use packages to facilitate data modeling, processing, and more.


Keras is a deep learning application programming interface (API) used in machine learning. It facilitates the processing of large amounts of unstructured data and enables fast experimentation in data modeling. Keras is written in Python, which makes it a fast technology for machine learning.

Keras also connects to the TensorFlow 2.0 ecosystem, ensuring every machine learning step works seamlessly to produce real data. ML engineers can use Keras for hyperparameter training, data management, experimentation, and deployment solutions.


JavaScript facilitates machine learning for front end developers. JavaScript’s TensorFlow library helps ML engineers build, run, and retain machine learning models. JavaScript can run on multiple devices securely. The JavaScript graphics API ensures security and enhances performance.


TensorFlow is an open-source AI library for machine learning. It features a wide range of tools and resources to help developers and ML engineers build incredible machine learning applications. TensorFlow works for neural networks and is perfect for dataflow programming across several tasks.

TensorFlow also offers excellent accuracy for graphs, debugging, data pipelines, and scalability. Furthermore, you can always look for support and solutions from the massive TensorFlow community.


Java is one of the top coding languages after Python. It is a fast and reliable language for machine learning because of its efficiency in data science. ML engineers and data scientists can conduct data analysis and build solid ML applications using Java. It is also applicable to a broad range of devices.

How Can You Become a Machine
Learning Engineer?

You can become a machine learning engineer by getting an education in computer science or data science. The learning path for any job in machine learning is the same because you need to attain the necessary education, get entry-level experience, and enhance your skills on the job.

However, learning never ends for an ML engineer. This is because the field is constantly evolving. ML engineers continue to attend bootcamps, further their education in university, or hone their skills through online courses. The more you study and research new trends, the better you will be at your job.

Below are three essential learning paths for a machine learning engineer.

Machine Learning Education Paths


Bootcamps have become a critical education path for professionals in the tech industry. They are short-term intensive programs covering a wide array of topics in tech. Attending a machine learning boot camp helps you understand everything connected to ML, including algorithms, models, and data analytics.

Bootcamps use an efficient and comprehensive curriculum that focuses on essential topics. You can also receive career guidance from experts. Some boot camps even collaborate with hiring partners to help you secure a job. Some of the best ML bootcamps include Springboard, Flatiron, and Coding Dojo.


The minimum education level for a machine learning engineer is a master’s degree. You can pursue a Master’s Degree in Artificial Intelligence, which focuses on machine learning and other areas of AI.

However, an entry-level machine learning engineer requires a bachelor’s degree. Generally, you can pursue a Bachelor’s Degree in Computer Science or Data Science. This degree takes about four years. It allows students to acquire fundamental skills for machine learning.

You will acquire programming skills, advanced statistical and analytical skills, and coding skills. Students also explore software development and data modeling. To further your career, you can also get a doctoral degree.


Self-study involves gaining more knowledge through online courses. There’s a broad range of massive open online course (MOOC) providers, such as Udemy and edX, who provide reliable and certified ML courses. Most of these courses are self-paced, allowing you to create a personal schedule.

You can start by taking some free courses to gauge your ML skills before undertaking a paid course. One of the best ML online courses is Machine Learning A-Z: Hands-on Python and R in Data Science by Udemy. It offers plenty of code examples to help you understand machine learning.

What Companies Are Hiring Machine Learning Engineers?

Companies in healthcare, finance, manufacturing, advertising, entertainment, and more are hiring machine learning engineers. With organizations rushing to digitize data, ML is dominating across all industries. ML engineers are in high demand because their skills are necessary for ensuring workflow.

Popular sectors for an ML engineering job include transportation, where self-driving cars are joining the market. ML engineers create software that ensures the cars conduct accurate traffic predictions by installing speech recognition software.


What skills do I need to become a machine learning engineer?

Coding languages, analytical and math skills, and communication skills will help you become a machine learning engineer.

How long does it take to become an ML engineer?

The time frame depends on your education path. However, employers require at least a bachelor’s degree, which takes four years.

Is ML a good career path?

Yes. ML engineers are in high demand, and the job comes with great benefits.

Do I have to attend an ML Bootcamp?

No, ML bootcamps aren’t mandatory. However, it is an excellent education path if you want to hone your ML skills.

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