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Machine Learning Engineer Career Guide

What is a machine learning engineer?

A machine learning (ML) engineer is a professional who combines knowledge of data science with software engineering to design and build algorithms and models that enable machines to learn from and make decisions based on data. In an era where data is the new oil, these professionals are vital to unlocking the potential of vast amounts of information, transforming it into actionable insights and automated processes..

They work on the cutting edge of technology, helping to drive advancements in fields such as artificial intelligence (AI), robotics, and data analytics. Their work can be found in various sectors, from technology and finance to healthcare, and has the potential to revolutionize how we live and work.

Duties and responsibilities

ML engineers are responsible for creating machine learning models and retraining systems. They manage the entire lifecycle of machine learning application development, including understanding the business problem, analyzing the data, preparing the data, applying relevant algorithms, evaluating their effectiveness, and deploying the models into production.

These engineers often work closely with data scientists to transform machine learning models into APIs so that other applications can access them. They also need to ensure that the machine learning applications they build are scalable and reliable..

They also monitor machine learning systems’ performance to make necessary updates or adjustments. They continuously research and implement new machine-learning technologies and methodologies to improve existing systems.

Work environment

ML engineers typically work in an office setting, though remote work can also be common, especially due to recent shifts in working patterns. Due to the highly technical nature of their work, their environment is usually equipped with powerful computing resources and state-of-the-art software tools. The role also involves extensive collaboration with other teams, such as data scientists, analysts, IT professionals, and business strategists. So, while they spend a lot of time working independently on complex problems, they also need to be effective team players.

Typical work hours

The work hours for an ML engineer usually follow a standard 9 am to 5 pm, Monday through Friday schedule. However, like many technology roles, this can vary depending on project deadlines or the need to troubleshoot urgent issues. The nature of their work is such that they can often work remotely, providing flexibility in their schedule..

How to become a machine learning engineer

This career guide section outlines how to become an ML engineer. We highlight the required steps, from your initial studies to the job application and interview stage.

Step 1: Complete high school

The first step starts with completing your high school education. Focus on classes that emphasize mathematics, physics, and computer science, as these subjects are fundamental to the career path.

Step 2: Earn a bachelor’s degree

Earning a bachelor’s degree in computer science, mathematics, information technology, or a related field is the second step. This degree should give you a broad understanding of algorithms, data structures, and coding languages — all essential skills for machine learning engineers.

Step 3: Specialized study in machine learning

Take courses or electives in machine learning, data mining, and statistical pattern recognition. These subjects present the more complex aspects that an ML engineer will run into daily. Universities and online platforms offer programs and courses in these areas.

Step 4: Obtain a master’s degree (optional)

A master’s degree in machine learning or a closely related field can offer more intensive, in-depth courses and research opportunities, giving you a competitive edge in the job market. While optional, it could greatly benefit your career in the long run.

Step 5: Gain practical experience

Try to get hands-on projects or internships in machine learning. This experience will solidify your learning and provide real-world context to theoretical concepts. It’s an opportunity to create a robust portfolio to show potential employers.

Step 6: Learn programming languages

Professionals in this role should be well-versed in programming languages like Python, R, SQL, and Java. Proficiency in these languages is critical for designing machine learning systems.

Step 7: Certification

Certification in machine learning or data science can further boost your credentials, making you even more attractive to potential employers. Several companies and institutions offer these certifications.

Step 8: Job application and interviews

Once you have completed all the necessary education and gained some practical experience, you can begin applying for jobs as a machine learning engineer. Prepare for a series of interviews that will test your technical acumen and problem-solving approach.

How much do machine learning engineers make?

ML engineer salaries vary by experience, industry, education, location, and organization size. The compensation for this role can significantly increase with proven skills in advanced algorithms and programming languages, as well as higher education degrees like a Ph.D.

Highest paying industries

  • Information Services and Data Processing – $139,170
  • Computer Systems Design – $138,200
  • Scientific Research and Development Services – $136,040
  • Software Publishers – $135,330
  • Management of Companies and Enterprises – $133,530

Highest paying states

  • California – $139,620
  • Washington – $138,400
  • New York – $137,900
  • Virginia – $136,960
  • Massachusetts – $136,700

Browse ML engineer salary data by market

Types of machine learning engineers

Below, we explore common career types and areas of specialization for ML engineers.

Algorithm specialist

In this role, the central focus revolves around formulating machine learning models. It entails creating intricate algorithms that teach systems how to learn and make predictions. With a deep understanding of data analysis and complex mathematical concepts, these professionals have the skills to build systems that can learn without being explicitly programmed.

Data scientist

This specialization involves utilizing machine learning to extract valuable insights from massive datasets. By building machine learning models, these experts can interpret and analyze data in ways that can lead to significant discoveries and solutions. Typically, data scientists have strong skills in statistics, probability, and data visualization.

Deep learning engineer

Professionals in this area work with deep learning frameworks that simulate human learning and reasoning skills. They employ neural networks to solve complex problems and help systems learn tasks that they can improve over time. A strong background in computer science and programming languages, such as Python or Java, is often required for this role.

Natural language processing engineer

This branch of ML engineering is concentrated on teaching machines to understand human languages. The expertise demanded by this role includes both linguistics and computer science, as these professionals work on significant projects like voice recognition and language translation systems.

Computer vision engineer

This specialization is focused on enabling computers to interpret and understand visual data. By developing machine learning algorithms, these engineers help systems recognize images and their elements, making them useful for applications such as facial recognition, object detection, and even medical imaging interpretation.

Top skills for machine learning engineers

This career guide section outlines the skills and abilities that will help you find success as an ML engineer.

Programing languages

The key to being a good machine learning engineer is proficiency in a variety of programming languages. These can include Python, R, and Java. An advanced understanding of these languages will allow you to build complex algorithms and work on high-level projects.

Mathematical expertise

Mathematical knowledge is crucial in this field. A strong command of linear algebra, statistics, and calculus can help build efficient algorithms and understand how they work on a fundamental level.

Understanding of machine learning algorithms

Having a deep understanding of machine learning algorithms is key in this career. Engineers need to understand a wide range of complex concepts, such as natural language processing, neural networks, and reinforcement learning.

Communications skills

Communication skills are often underrated in technical roles, but they are vital in machine learning engineering. The ability to explain complex concepts to non-technical team members and stakeholders can help bridge the gap between tech and business teams, aiding in the successful completion of projects.

Problem-solving ability

Being a problem solver is another important aspect of this field, as you’ll face difficult puzzles to solve regularly. These can involve debugging code, refining algorithms, or finding the best way to gather data. Strong problem-solving skills can help you tackle these roadblocks efficiently and effectively.

Machine learning engineer career path options

As an ML engineer, you’ve already set foot on a career path with boundless possibilities. Your trajectory from this point forward can span numerous industries and functional areas. Broadening your experience and developing specialized skills can help you ascend the career ladder or transition to a new field altogether.

Career progression

After building a solid foundation in your role, you can pursue lucrative avenues such as a senior or principal ML engineer or even architect. In these advanced positions, you’ll typically oversee more significant machine learning projects, mentor junior professionals, and make higher-level strategic decisions.

If management is a goal, the role of a team lead or head of machine learning could be an ideal move, where you’ll lead a team of engineers and drive project success. Alternatively, a shift toward a data science manager can offer a blend of technical expertise and managerial responsibilities, focusing more on data analysis and statistical methods along with machine learning.

Some professionals, equipped with appropriate knowledge and passion, set their sights on a more directional role as chief technology officer (CTO) or consider founding a tech startup. Both paths allow the utilization of acquired skills in defining a business’s technical vision and strategy, with the bonus of being intimately involved in a company’s success.

Cross-functional opportunities

Alternatively, for those who wish to branch out, there are numerous roles in related fields. For example, transitioning into a data scientist role may be a natural next step, where the work involves more statistical analysis and less model building and deployment. Another option could be pursuing a position as an AI engineer, blending machine learning skills with artificial intelligence to develop AI models and applications.

Additionally, roles such as product or project manager in AI or data products could be an option for professionals who want to move away from hands-on coding and focus more on high-level strategic and management tasks. Here, your machine learning background would give you a distinct edge in understanding the product constraints and potential and facilitating better decision-making.

The field of ML engineering has experienced significant growth over the past few years. This surge is notably due to the increasing demand for technologies that can autonomously learn and improve from experience. Companies across various industries have shown interest in incorporating machine learning algorithms into their operations, with applications ranging from customer service bots to complex decision-making systems. The automobile and healthcare industries, in particular, have demonstrated a significant investment in machine learning, driving forward autonomous vehicle tech, and predictive healthcare systems, respectively. It’s becoming apparent that this profession isn’t limited to technology firms; businesses from almost every industry are seeing the merit in implementing machine-learning capabilities.

There has also been a steady trend in the demand for engineers skilled in specific programming languages popular in the machine learning field. Python, in particular, has emerged as a widely preferred language due to its simplicity and robust libraries that make it easier to implement algorithms. Additionally, the growing demand for real-time processing and decision-making capabilities has seen an uptick in the use of languages like Java and C++. The ability to diversify and acquire skills in multiple programming languages can offer a significant competitive advantage in this field.

Employment projections

Given the rising interest in machine learning across numerous industries, this profession is expected to grow significantly faster than other occupations. The market is still early in the adoption curve, with a very long runway ahead. The demand for ML talent will likely outpace supply for the foreseeable future.

Machine learning engineer career tips

Understand key frameworks and programming languages

An ML engineer must be familiar with several programming languages, including Python,e C++, and Java. One of the ways to stand out in your career is to gain a deep understanding of these languages, along with other relevant frameworks, such as TensorFlow and PyTorch. These tools are extensively used in designing, training, and deploying machine learning models.

Be knowledgeable in data management

To create effective machine learning models, it’s vital to know how to manage and manipulate data. Understanding data pre-processing, feature selection, and data transformation techniques are key responsibilities you must be adept in to succeed in this role.

Stay current with the latest research

The field of machine learning is rapidly evolving. By keeping up to date with the latest academic literature and breakthroughs, you can apply these advancements to your work and stay ahead in this competitive environment.

Foster strong communication skills

It might not seem obvious, but good communication skills are essential for someone in this technical role. You will often be required to explain complex data models and algorithms in a way that stakeholders and non-technical team members can comprehend. Excellent oral and written communication skills can greatly help you in this aspect.

Build a professional network

Part of succeeding as an ML engineer is knowing the right people. Networking with other professionals in your field can lead to new career opportunities and help you remain aware of industry developments.

  • Association for the Advancement of Artificial Intelligence
  • Professional Association for SQL Server
  • Data Science Association

Invest in continuous learning

Given the rapid advancement of machine learning, continuous learning is invaluable. Pursuit of relevant certifications and skill upgradation through various modes of learning show dedication toward advancing in your role.

  • Professional Certificate in Deep Learning from IBM
  • Machine Learning Certification by Stanford University (Coursera)
  • Postgraduate Program in AI and Machine Learning (Purdue University)

Where the ML engineer jobs are

Top employers

  • Google
  • Facebook
  • Microsoft
  • Amazon
  • Apple

Top states

  • California
  • Washington
  • New York
  • Texas
  • Massachusetts

Top job sites

  • zengig
  • Indeed
  • LinkedIn
  • Monster
  • AI Job Pro


What are some of the key skills required to be successful as an ML engineer?

Successful practitioners in this field display a strong foundation in computer science, statistics, and mathematics. Beyond specific technical skills, effective problem-solving capabilities, an analytical mindset, and a curiosity about patterns and trends are also crucial.

What is a typical workday of an ML engineer??

A typical day could involve data exploration and cleaning, model building, testing algorithms, interpreting and communicating results, or collaborating with other team members on larger projects. The role is quite varied and requires adaptability and broad skills.

Which industries employ machine learning engineers?

This profession is prevalent in several areas due to its versatility. ML jobs are common in technology but fundamental to healthcare, finance, transportation, and manufacturing, among other industries.

What are the typical education requirements for an ML engineer?

This role typically requires a bachelor’s degree in computer science, mathematics, or a related field. However, due to its highly specialized nature, many employers prefer candidates with advanced degrees focusing on machine learning or artificial intelligence. Proficiency with specific programming languages such as Python or Java is often required as well.

What part does teamwork play in ML engineering?

Although this profession involves a lot of independent research and problem-solving, it’s essential to communicate effectively with other team members. You’ll often need to collaborate with other engineers and stakeholders to complete projects or clarify business objectives.

Are there ML certifications available that will improve marketability?

Certifications can highlight your expertise and dedication to the field. Consider pursuing credentials like the TensorFlow Developer Certificate or the Microsoft Certified: Azure AI Engineer Associate to enhance your profile and stand out in this competitive profession.

What is the career progression in machine learning?

Entry-level roles typically involve working under the supervision of a senior engineer or lead. As you gain more experience and demonstrate advanced skills, you may move into roles with more responsibility, eventually leading a team or overseeing a department. This profession also offers opportunities to move into fields like data science or artificial intelligence research.

What can I do to stand out in my job applications as a machine learning engineer?

Beyond relevant education and certifications, highlighting real-world experience can make your resume stand out, including internships, relevant research projects, or even personal projects utilizing machine learning. Demonstrating your problem-solving abilities through specific examples can also make a strong impression.

How important is continuous learning in machine learning?

Continuous learning is critical due to the rapidly evolving nature of technology and machine learning applications. A passion for learning and the capability to adapt to new paradigms and tools is vital for long-term success.