How to Become a Machine Learning Engineer

Machine Learning
Jacob SimonJacob SimonLast updated

Considering a career as a machine learning engineer? Are you curious about transitioning from software engineering or data science into an ML role?

This career guide provides an overview of the machine learning engineer role, top hiring companies in the field, and advice on how to land a job.

This ML career guide accompanies Exponent's machine learning system design interview course, trusted by hundreds of engineers to ace their ML interviews.

Sneak peek:
- Rubric for ML system design interviews.
- Designing a data pipeline.
- Top ML system design interview questions.

The last few years have seen machine learning jump into the mainstream.

And with AI and ML-powered technologies gaining steam, so is the demand for machine learning engineers.

Nico Thiebaut is a machine learning engineer at Hired, the job search marketplace.

Role Overview

Machine learning engineers are software engineers who develop software that can learn from data and make predictions.

Companies like Google, Microsoft, Amazon, and Facebook hire machine learning engineers because they need them to develop algorithms to find patterns in data and subsequently make predictions.

Other industries, like banking, advertising, healthcare, and social media, require machine learning engineers because they use AI and ML-powered systems to detect fraud, predict behavior, and improve user outcomes.


The primary goal of a machine learning engineer is to convert data into actionable insights and products.

Machine learning engineers are responsible for designing, developing, and maintaining algorithms and models that make predictions from data.

This OpenAI job posting for an ML engineer highlights some of these key responsibilities:

  • Construct a robust pipeline for data management, model training, and deployment to consistently enhance the Moderation model.
  • Design, develop, and sustain a robust, scalable data management pipeline. Establish standards for versioning and data quality control.
  • Ensure the pipeline can handle data relabeling requests stemming from content policy changes.
  • Develop a pipeline for automated model training, evaluation, and deployment. This includes an active learning process and calibration and validation data refresh routines.
  • Collaborate closely with stakeholders from product, engineering, and content policy departments for the long-term improvement of moderation models. This applies to external release and internal use cases across various projects focused on model safety.
  • Conduct research on the latest techniques and methods in deep learning and natural language processing to enhance the moderation model across a range of unwanted content categories.

Skills and Qualifications

Machine learning engineers design and develop applications and systems that use data to solve business problems.

They work alongside data analysts and other software engineers to implement algorithms and models. This collaboration helps in collecting and processing data to make predictions.

Using programming languages like Python, Java, and C++, they create self-learning software capable of automating predictive models.

Machine learning engineers design scalable machine learning pipelines, source, and clean datasets, and use statistical analysis to enhance machine learning model quality. They also play a role in deploying models, overseeing infrastructure, and monitoring systems in production.

Technical Skills

  1. Programming Languages: Python, R, Java, and C++ are commonly used in machine learning roles.
  2. Mathematics, Probability, and Statistics: These disciplines will help you create and test machine learning models.
  3. Algorithms and Frameworks: You don't need to build machine learning algorithms from the ground up but should understand them thoroughly. Get to know frameworks like Scikit-learn, TensorFlow, and PyTorch.
  4. Software Engineering and System Design: Understand software engineering principles and best practices for building large systems.
  5. MLOps: This is about making the process of getting machine learning models to production more efficient. You should know about tools and practices for deploying, monitoring and maintaining these systems.

Soft Skills

  1. Communication: You need to communicate well. You'll work with different people like data scientists, product teams, and engineers, and you need to explain complex technical concepts to them.
  2. Problem-Solving: You'll be solving tough problems in machine learning projects. You need strong problem-solving skills and the ability to think critically and creatively.
  3. Fast Learner: Things change quickly in machine learning. You need to love learning and be able to keep up with new tools and methods.

ML Career Paths

Average total compensation for MLEs in Los Angeles, for example.

Machine Learning Researcher

A Machine Learning Researcher is a person who uses data to create software for large companies that want to use machine learning research to make a profit in the fields of finance, marketing, or healthcare.

This research is then used in building autonomous computers that learn from experience and evolve.

Because machine learning researchers are often employed at large companies, they're paid well.

Some examples of machine learning research jobs include:

  • Some ML Researcher positions at Apple are focused on cutting-edge research in various machine learning domains, requiring a PhD or equivalent experience, and a strong background in machine learning, deep learning toolkits, and mathematics.
  • Some ML Researcher positions at Zoom are aimed at new graduates currently pursuing or holding a Master's or PhD in Computer Science, Electrical Engineering, or related fields. The role requires experience in speech recognition and processing, strong programming skills in Python, and familiarity with machine learning frameworks and speech recognition toolkits.

Data Scientist

The data scientist role is the most sought-after job in machine learning and artificial intelligence, making an average salary of $138,365 annually in the U.S.

Data scientists make sense of information by creating algorithms to find patterns in large amounts of data and automate business processes.

They also analyze data to create predictive models or algorithms to solve real-world problems and guide businesses to improve customer service, sales, marketing, and more decisions.

Some examples of data science jobs with an ML focus include:

  • The Data Scientist role at Disney requires a Bachelor's or Master's degree in a quantitative field such as Computer Science, Engineering, or Mathematics, along with at least 3 years of experience in developing machine learning models and conducting statistical analysis. You'll need a deep understanding of ML concepts and statistical methods, proficiency in Python and ML frameworks, ability to interpret data for business decisions, and experience in integrating data science solutions into business operations.
  • The Applied Data Scientist role at Snap Inc. requires an M.S. or Ph.D. in a quantitative field like CS, Math, Physics, Statistics, or Econometrics, with 9 years of experience in machine learning, including full lifecycle ML model development and causal inference techniques. Proficiency in SQL, Python, or R, strong statistical knowledge, and experience with deep learning frameworks like TensorFlow or PyTorch are essential, along with the ability to initiate projects, communicate effectively, and provide cross-team leadership in data science.

Machine Learning Engineer

A machine learning engineer uses algorithms to build predictive models for solving problems in a specific industry or field.

They regularly collaborate with data scientists responsible for analyzing large amounts of data to identify patterns and correlations that machines can use to make decisions or predictions about future events.

NLP Scientist

A Natural Language Processing (NLP) Scientist gets an average of $122,265 annually. An NLP Scientist bridges the gap between human communication and computer comprehension by using computers to understand, interpret, and manipulate human language.

They do this by using computational linguistics and computer science. The demand for NLP Scientists has risen in the past few years due to the rapid development of technology and the increasing popularity of using human language as a platform for communicating with computers.

Human-Centered Machine Learning Designer

A Human-Centered Machine Learning Designer is a new role that is being created by companies and organizations around the world. These designers are responsible for bringing together the best of both worlds—machine learning and human creativity.

This role is all about designing products, services, or experiences for people. It is about creating an experience that feels like it was designed by a human but was actually generated by a machine.

Human-Centered Machine Learning Designers typically earn about $110,000 on average.

  • Toyota's Human-Centered AI ML Internship requires expertise in Generative AI (image, text, 3D modalities), computer vision, NLP, and a background in a PhD program with a history of research project execution and publications. You should be capable of working autonomously, driving technical plans, and aiming to produce work publishable in academic journals.

Becoming a Machine Learning Engineer

Becoming an MLE isn't an easy endeavor.

Get Educated

Machine learning engineers typically require significant training and experience to be effective. After all, artificial intelligence is a complex field.

The educational requirements for a machine learning engineer position will vary depending on the company or institution.

For instance, Google's ML job requirements are

  • Bachelor's degree or equivalent practical experience
  • 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript)
  • 3 years of experience in a technical leadership role; overseeing strategic projects, with 2 years of experience in a people management, supervision/team leadership role
  • Experience in Machine Learning Infrastructure
  • Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency)
Google's ML engineer job requirements include multiple years of experience and education.

Some companies require that you have an undergraduate degree in computer science, mathematics, or engineering with at least a few years of work experience.

Other companies don't require any specific educational background. Still, they prefer candidates with experience in data science, statistics, machine learning, and artificial intelligence.

But at the minimum, you should ideally have a computer science, statistics, or mathematics degree.

Still, you are not necessarily disadvantaged if you don't have a degree. Of course, you can always pick up the skills through online courses or through a machine learning certification program—but having an undergraduate degree will speed up the learning process and provide a level of credibility to your ML knowledge that'll be recognized by potential employers.

Learn Several Programming Languages

The primary tool of the machine learning engineer is programming.

Again, machine learning is a complex field with several different dimensions. As such, a machine learning engineer must have deep knowledge and competency with several programming languages.

Six, in particular, are especially relevant for building machine learning systems:


While Python doesn't precisely make mincemeat of machine learning, it makes it easy to spot issues and apply quick fixes. In addition, Python has plenty of libraries for machine learning engineers specifically to accelerate their work.

Another reason Python is so popular is its availability on various platforms. It has been ported to Linux and Mac OS X and has recently found its way into mobile apps.


These programs are optimized for speed, memory, and resource usage. C++ also provides a flexible type system with reusability features like templates and exception handling. The language is compiled into highly efficient native code.


Another cornerstone of machine learning is the R programming language. R is widely used in statistical computing, data sampling, and analysis and boasts an extensive range of packages to help with machine learning and data analysis tasks.

Java and JavaScript

Java is a general-purpose programming language. It is widely used in machine learning because of its libraries, frameworks, and tools used to build complex algorithms.

JavaScript, on the other hand, is a client-side scripting language. For machine learning to work, the computer must understand and interpret data.

JavaScript is also used with other programming languages, such as Python or C++. It is used to visualize and present data in different forms, such as charts or graphs.


Shell is a powerful tool in the arsenal of any machine learning engineer. It can create algorithms, predictive models, and applications through an intuitive interface and with minimal human intervention.

Moreover, it gives you a high-level, general-purpose programming language focusing on data processing and statistical computing. This makes it suitable for data science, predictive analytics, and other related fields in machine learning.

Develop the Necessary Skills

Fundamentals of Computer Science

Because machine learning is a subfield of artificial intelligence, you can't get around having strong knowledge of computer science fundamentals:

  • Understanding the essential components of computer architecture, such as bandwidth, memory, cache, memory bus, and CPU architecture, is necessary to design more efficient implementations.
  • A solid understanding of data structures such as stacks, trees, lists, queues, arrays, and even graphs can help you write efficient code for different use cases.
  • Being comfortable with different types of computational algorithms, such as neural networks, dynamic programming, and hash tables, is necessary to build machine learning models.

Data Modeling and Evaluation

Data modeling and evaluation skills help make sense of data that would otherwise be difficult to interpret.

It also helps build models that are more robust and accurate than those that are created without the use of data modeling.

For example, in machine learning, data modeling is used for:

  • Transforming raw data into a structured format that can be used by machine learning algorithms.
  • Finding patterns in large datasets, identifying correlations, and making subsequent predictions.
  • Creating data models that predict future outcomes on various topics such as marketing, fraud detection, customer service, etc.
  • Building better models and making them more accurate.

Probability and Statistics

When dealing with machine learning, it is essential to understand probability and statistics.

These concepts help us comprehend how we can make better decisions using data.

For example, statistical measures such as conditional probability, decision processes, mean, median, and variance help machine learning by providing estimates of the relative likelihood of different outcomes.

Likewise, probability measures help machine learning engineers understand the decision-making process.

Machine learning is heavily influenced by statistics and probability, as they help improve the performance and accuracy of predictive models.

System Design

Machine learning engineers often have to design software systems that use statistical analysis of data sets—such as customer behavior patterns or stock market transactions—to help businesses make better decisions about their products or services.

For instance:

  • Machine learning in customer service might use a suitability analysis to determine which customers are ready to be contacted by their respective representatives.
  • Demand generation specialists might use data mining and predictive analytics software to find new ways of promoting products to increase sales.

No matter how they're used, system design skills are vital for designing or improving machine learning systems and products.

More Resources

We've connected thousands of job seekers in countless tech roles with expert-level courses and resources to prepare them for their upcoming interviews.

If you're interested in more machine learning resources, be sure to check these out:

💬 Study up on example machine learning conceptual interview questions.

📖 Read through our company-specific engineering interview guides

👯‍♂️ Practice your behavioral and interviewing skills with our mock interview practice tool

Learn everything you need to ace your machine learning interviews.

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