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.
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.
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:
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.
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:
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:
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.
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.
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 an MLE isn't an easy endeavor.
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
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.
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 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.
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.
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:
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:
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.
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.
No matter how they're used, system design skills are vital for designing or improving machine learning systems and products.
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
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