How to Become a Machine Learning Engineer was originally published on Exponent.
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The last few years have seen machine learning jump into the mainstream. Machine learning is a class of algorithms that can automatically acquire, interpret, or generate intended results without human involvement.
And with AI and ML-powered technologies gaining steam, so is the demand for machine learning engineers.
This handy guide will answer all your burning questions for those wondering about how to become a machine learning engineer. Here, you will learn:
- What A Machine Learning Engineer Does.
- The Scope of Machine Learning and Career Potential.
- The Skills You Must Have to Become a Machine Learning Engineer.
- What Courses You Must Take and the Degrees You’ll Need to Earn.
And, of course, we’ll get into the nitty-gritty on how to get a job as a machine learning engineer.
Table of Contents:
- What Is a Machine Learning Engineer?
- What Does a Machine Learning Engineer Do?
- What Makes a Good Machine Learning Engineer?
- What Are the Career Paths in Machine Learning?
- Becoming a Machine Learning Engineer
- Ace the Machine Learning Interview With Exponent
What Is A Machine Learning Engineer?
Machine learning engineers are programmers who develop software that can learn from data and make predictions. They also develop algorithms to solve problems and find solutions in computer science and engineering.
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.
In addition, other industries, like banking, advertising, healthcare, and social media, require machine learning engineers because they use AI and ML-powered systems for different applications.
What Does a Machine Learning Engineer Do?
Machine learning engineers are responsible for designing, developing, and maintaining the artificial intelligence systems involved in machine learning.
These tasks involve a wide variety of activities. However, the job of a machine learning engineer can be broken down into five primary dimensions:
- Designing machine learning algorithms: Understanding the algorithm’s desired outcome and figuring out how to get there.
- Building machine learning models: Implementing the design in code.
- Testing machine learning algorithms: Running and debugging it to ensure it works as intended.
- Maintaining machine learning algorithms: Ensuring it continues running smoothly and without any snags.
- Updating machine learning algorithms: Implementing quick, remedial measures if it’s not performing as intended.
What Makes a Good Machine Learning Engineer?
Machine learning engineers are more proficient in software engineering than your average data scientist.
With machine learning and deep learning, engineers can identify customer needs and build software solutions with high accuracy and predictive power.
As a result, it’s much easier for them to develop a predictive machine learning model that improve customer retention and streamline operations.
Writing Clean Code
A good machine learning engineer knows how to write good, clean code. It is the most essential skill for them because it enables them to develop algorithms to make sense of data and develop predictive models.
Understanding Software Development Methodologies
A solid understanding of software development methodologies, different architectures, and familiarity with other IDEs are vital to becoming a good machine learning engineer. Their ability to build, test, and maintain large-scale software architectures entirely rests on this understanding.
Proficient in Many Programming Languages
Engineers working in machine learning require a broad knowledge of Python, R, Java, C++, and other languages in the field. They must also have knowledge of algorithms such as linear regression and support vector machines. This helps them learn new skill sets quickly, work with multiple data sets simultaneously, and process large volumes of data.
Sound Algorithmic Thinking
A good machine learning engineer should be able to think about the problem at hand holistically and design a model that can solve it. They should also be able to think about what level of accuracy is needed for the model they’re building and how much time it will take them, on average, to build the model.
Strong Theoretical Foundation in Mathematics, Statistics, and Computer Science
Theoretical knowledge is vital for the machine learning engineer to design effective models that can be applied across many domains, such as natural language processing (NLP), computer vision, and speech recognition.
Machine learning engineers require a solid theoretical foundation in mathematics, statistics, and computer science. Concepts such as probability theory, linear algebra, and calculus help them execute machine learning functions such as:
- Predictive analytics
- Statistical analyses
- Recommendation engines
- Neural networks
- Support vector machines
- Search algorithms
- Natural language processing
- Data mining
- Big data analysis
- Information retrieval
What Are The Career Paths in Machine Learning?
Machine learning is an emerging and rapidly expanding field that is quickly changing the world of AI. As such, a machine learning career opens up several exciting and lucrative avenues. And these are the five best career options for machine learning:
Machine Learning Researcher
A Machine Learning Researcher is a person who uses data and computer programming 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 employed by more prominent companies, their average salaries tend to fall in the $125,000-$155,000 range.
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’ job is to make sense of information by creating algorithms that can 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.
Machine Learning Engineer
A machine learning engineer uses machine learning algorithms to build predictive models for solving problems in a specific industry or field.
They regularly collaborate with data science professionals responsible for analyzing large amounts of data to identify patterns and correlations that machines can use to make decisions or predictions about future events.
As of October 2022, machine learning engineers’ average salaries range from $107,385 to $134,377.
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.
Becoming a Machine Learning Engineer
Becoming a Machine Learning Engineer isn’t an easy endeavor. You will need the proper knowledge, skills, degrees, specialized coaching programs, hard work, and dedication to break into the machine learning field.
We will now find out where to begin your journey to becoming a machine learning engineer, how to get there, and what you can expect along the way.
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.
Some companies require that you have an undergraduate degree in computer science, mathematics, or engineering with at least 2 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.
There are countless coding languages out there. However, not all of them are useful for machine learning engineers.
The Top 10 Highest Paying Programming Languages of 2022
Want to know what programming languages pay the most? Check out the highest paid salaries for each language.
ExponentAnthony Pellegrino
Six, in particular, are especially relevant for building machine learning systems:
Python
Python is a general-purpose programming language designed to be easy to read and write. It is straightforward to learn and has a vast number of libraries available.
While Python doesn’t precisely make mincemeat of machine learning (it does have its own complex algorithms, after all), 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.
C++
C++ has evolved over time to become an efficient and powerful programming language. As a result, it has been used for many purposes, including software development, game development, scientific computing, embedded systems, and machine learning.
Machine learning engineers typically use C++ to write code that can be compiled into an executable program that runs on the target device.
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.
R
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.
This makes it easier for experts in machine learning to make intelligent software applications that can use a lot of data to find valuable insights and predictive models.
Java and JavaScript
Java and JavaScript are the two most common programming languages used for machine learning.
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
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
Your path to becoming a machine learning engineer will be a bumpy ride if you lack the necessary set of 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.
Ace the Machine Learning Interview with an Exponent Coach
There’s no getting around it: machine learning engineering interviews are incredibly challenging.
Even the most qualified and knowledgeable of candidates can struggle when answering ML interview questions such as:
- What are the best evaluation metrics for a loan model for a bank?
- How can Stripe use data to predict the best time to retry a transaction?
- Design a landmark recognition system.
One of the best ways to set yourself up for success is by hiring an expert interview coach. These coaches are some of the best resources for practicing your interview skills and becoming comfortable and confident with the machine learning interview process.
Best Places to Find an Interview Coach
An interview coach can help you prep for upcoming interviews. Practice mock interviews and get guidance from the top interview coaching services.
ExponentExponent Team
Of course, despite what you may think, the goal of interviewing is not to find a perfect candidate but to find someone with the right skill set for a given role and company culture. This means that your ultimate success (or lack thereof) will rely on much more than simply demonstrating your aptitude for machine learning skills.
An Exponent interview coach can help you with the following:
- Prepare and understand what you should expect in the interview process.
- Know how to answer machine learning case questions in a way that will make them stand out.
- Prepare you for dealing with difficult questions by using their expert knowledge of machine learning and engineering interviews.
- Get you hired for a machine learning job even if you don’t have any experience in this field.
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Interested in finding a coach for your upcoming interview? Click here to schedule a consultation today!
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 software engineering and machine learning related resources, be sure to check out the following:
💬 Study up on example software engineering interview questions
📖 Read through our company-specific engineering interview guides
👯♂️ Practice your behavioral and interviewing skills with our mock interview practice tool.
👨🎓 Take our complete Software Engineering Interview Course.
