What Does a Machine Learning Career Look Like?

Published on: Oct 17, 2022
By: Editorial Staff

These days, it seems like everyone is looking to build a machine learning career. Across lush Ivy League campuses and active subreddits alike, students and young professionals are plotting their next moves to break into an exceedingly hot job market.

How hot? In 2019, Indeed reported that postings for machine learning engineer jobs accounted for 179 of every one million job postings in 2018, with the total number of machine learning engineer postings growing by nearly 350% between 2015 and 2018.

Increasing demand has translated into high salaries for those with key machine learning skills. When ‘machine learning (ML) engineer’ topped Indeed’s Best Jobs of 2019 list, the reported average base salary was $146,085. This growth hasn’t leveled off just yet: LinkedIn placed machine learning engineer fourth on its 2022 Jobs on the Rise list, with a salary range of $72,600-$170,000 — a big improvement on the 2020 median US earnings of $41,535.

But what does a machine learning career look like in 2022? What education do you need, and what’s your path to progress? In this article, you’ll see that in the case of machine learning, these questions are more closely linked than they might initially appear. Read on to gain a better understanding of what a machine learning engineer does, and how graduate-level degrees can unlock higher rungs on the career ladder.

What kinds of machine learning careers are out there?

If you have machine learning skills and experience, there are a variety of high-demand, lucrative careers open to you, including:

Data scientist: A data scientist ideates and executes novel approaches that turn raw data into business insights and solutions using machine learning and other data analytics.

Data engineer: A data engineer focuses on building, streamlining, and maintaining the data pipelines that are central to data science, machine learning, and other data analytics.

Software engineer: A software engineer specializing in artificial intelligence or machine learning system design focuses on building and maintaining a particular product (an app, API, or service) that brings AI capabilities to consumers and businesses alike.

That said, the primary role for those with machine learning training — and the one we will focus on here — is that of a machine learning engineer.

What does a machine learning engineer do?

The primary responsibility of a machine learning engineer is to design, develop, and ship machine learning models — and then upkeep them once they’re shipped and deployed to a live product. An ML engineer might work on a team focused on building a particular product or feature, or they might work as a generalist and move between a variety of different types of projects.

Machine learning engineers can also be responsible for managing data systems and undertaking various kinds of data analysis, such as analyzing data sets from A/B tests on a machine learning system or algorithm. All this doesn’t take place in a vacuum. ML engineers are tasked with using machine learning skills to solve real-world problems, and so their efforts can have a measurable effect on the lives of real people.

For machine learning engineer jobs, however, education is a big factor in determining the exact make-up of your day-to-day responsibilities. As you’ll see in the next sections, much of the exciting work that draws people to the field in the first place only becomes realistic after graduate-level study.

Another important caveat is that, since machine learning is still a rather nascent field, there is no one standard career path as there might be for software developers. With that said, in the next sections we will lay out the different levels of machine learning education and give you an idea of the kinds of machine learning engineer jobs each degree can unlock.

Machine Learning Bachelor’s Degree

Students looking to study machine learning as part of a Bachelor of Arts (BA) or Bachelor of Science (BS) course of study have options when it comes to picking a major. While most instruction in machine learning is still taking place through artificial intelligence or machine learning specializations added to traditional computer science majors, standalone AI and ML majors are becoming increasingly popular.

Regardless of home department, the curriculum will be largely the same. Students will take foundational courses in math, statistics (e.g. Multivariable Calculus, Linear Algebra, Probability), and computer science (e.g. Computing Systems I&II, Principles of Functional Programming Database Systems, Principles of Software Engineering), as well as machine learning-specific introductory courses and electives (e.g. Deep Learning, Natural Language Processing, Computer Vision).

Many programs also require a capstone project to be completed during your final year of study. Before that, students will generally take a course in AI ethics and policy, a writing and communication course, as well as distributional requirements in the social sciences and humanities. Often, undergrads will also have the opportunity to assist their professors in cutting-edge machine learning research.

While students may come out of their undergraduate degrees knowing how to write machine learning algorithms and implement machine learning models, in most entry-level machine learning engineer jobs, bachelor’s degree holders will focus on supporting senior managers with data pipeline work. In other words, most of their work won’t be around launching new ML models but maintaining and re-training those already in existence. See, for example, this entry-level machine learning engineer role at Amazon:

Machine Learning Engineer - Sponsored Products Search Relevance Team @ Amazon

Responsibilities

Drive direction of technical solutions and work on technologies including deep learning, AWS, and Auto ML.
Design, develop, and ship software to support scalable offline machine-learning pipelines and online service components.
Work closely with applied scientists to optimize the performance of ML modes, improve ML productivity, and advance technical foundations supporting innovation.

Base Qualifications

Programming experience with at least one software programming language.
1+ years of experience in software development.
Experience building ML infrastructure, data pipelines to train ML models, etc.
Experience with common ML techniques like pre-processing data, training, evaluation of classification, and regression models.
Experience building large-scale machine learning infrastructure for online recommendation, ads relevance and/or ranking, personalization, search, or similar area.

Glassdoor estimates an annual base salary for a role like this to be $134,852.

Machine Learning Master’s Degree

A master’s in machine learning is an advanced degree, usually offered by a computer science department, that’s intended to familiarize students with more advanced concepts in machine learning and give students the skills and other practical knowledge they need to undertake doctoral research or build and deploy artificial intelligence solutions in the industry.

While the majority of entering students will have completed bachelor’s degrees in computer science, master’s programs also accept students with backgrounds in other STEM fields, provided they have the requisite mathematics and programming skills, or even from the social sciences and humanities.

Machine learning engineer roles with more responsibility and more lucrative salaries often require — or “desire,” which in a competitive job market essentially means require — candidates to hold master’s degrees, sometimes even regardless of previous work experience. If bachelor’s degree holders in entry-level positions focus more on data pipelines, professionals in mid-level positions spend much more time developing and implementing machine learning models. Oftentimes, companies also like to see candidates have some industry experience. See, for example, this more senior machine learning engineer role at BofA:

Data Scientist/Machine Learning Engineer @ Bank of America

Responsibilities

Design and develop scalable ML/AI solutions to solve diverse business challenges.
Gather and analyze data to perform statistical analysis, identify key factors, and build comprehensive visualizations to report findings.
Utilize statistical methods to process, clean, and validate data for uniformity and accuracy.
Create and maintain end-to-end data pipelines and APIs according to business requirements.
Communicate analytic solutions to stakeholders and implement improvements as needed to operational systems.

Base Qualifications

5+ years in a data science role with proven record of implementing end-to-end ML/AI solutions into production.
5+ years of experience using statistical computer languages, such as R or Python (preferred)
3+ years of experience working with large data sets (> 1TB) and using big data solutions.
1+ year of experience with deep learning and NLP frameworks.
Excellent understanding of machine learning techniques and algorithms.
Rigorous understanding of statistics and ability to discern appropriate statistical techniques to problem-solve.
Proficiency with writing SQL queries.

Desired Qualifications

Master’s degree or PhD in computer science, applied mathematics, or related technical/scientific field.
Experience with data visualization tools, such as Tableau.
Prior work experience in the financial industry.

Indeed estimates an annual base machine learning engineer salary at Bank of America to be between $141,000 and $178,000.

Machine Learning PhD

Those looking for an opportunity to engage in more extensive research in machine learning will often enter Ph.D. programs within computer science departments. These kinds of programs are increasingly gaining popularity. Indeed, the percentage of graduating CS PhDs who specialize in artificial intelligence and machine learning has grown dramatically over the last ten years: Stanford’s AI Index reports that such students made up over 20% of all CS Ph.D. students in 2019, up 8.6% from 2010 and dwarfing all other CS specializations.

At the same time, pursuing a Ph.D. in artificial intelligence remains an exclusive club. With the rigorous requirements and lengthy time-to-degree (generally 4-5 years), it’s no surprise that the same survey counted less than 300 new Ph.D. students specializing in AI or ML graduating from American computer science departments in 2019.

In the US, students showing promise are frequently admitted to Ph.D. programs without a master’s degree, with the understanding that they will complete a master’s course of study (and the requisite comprehensive exams) before being allowed to begin doctoral research (a process usually known as “qualifying”).

While the primary focus in Ph.D. programs is on academic research, more and more newly-minted machine learning PhDs are entering the industry, working as high-level machine learning engineers and research scientists in artificial intelligence and machine learning labs like Google’s DeepMind and Meta AI. See, for example, this research scientist role at Meta:

Research Scientist - Input and Interaction @ Meta

Responsibilities

Ideate and articulate novel interaction techniques that will revolutionize the input stack on AR/VR.
Plan and execute cutting-edge research of forward-looking interactions that are uniquely enabled by multimodal input sources, including neuromotor (EMG) sensors.
Collaborate with designers, user researchers, and machine learning experts to unlock and perfect new capabilities to build interactions upon.
Create prototypes which explore input and interaction techniques in a variety of AR/VR environments and situations.
Evaluate the performance of input explorations and measure user response to AR/VR input solutions.

Base Qualifications

Experience working to design, execute, interpret, and present interactions research.
Currently has, or is in the process of obtaining, a PhD degree in Computer Science, Mathematics, Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
Experience on the full input stack in AR/VR, wearables, or gesture-based systems.

Glassdoor estimates the base annual salary for this role to be $162,778, with a whopping annual $119,340 in additional pay.

How to get started?

It all looks great on paper: apply to a program, earn a degree, and land a lucrative job offer. The problem is that there are lots of other smart people who have the same idea. Accordingly, already at the beginning of your journey, you’ll want to keep some best practices in mind.

It will be crucial to have a specific machine learning resume that outlines your experience and skill set, as well as a portfolio of projects that show off the practical application of your knowledge.

As you progress through school, you’ll want to keep the end goal in mind and ensure you’re setting yourself up for success by ensuring that your present actions are improving your future resume and portfolio. For help when the time comes, check out our machine learning resume guide.

To land a machine learning interview, it will also help to have a thriving network of professionals in and around machine learning and artificial intelligence.

You’ll get a great start at school, but you’ll want to also reach out to friends, family, and existing alumni networks. The more people you meet, the more you’ll learn about the field and the better chance you’ll have at finding someone willing to refer you for an open position.

Ensuring your success in the future means finding a bachelor’s, master’s, or Ph.D. program that is a good fit for your background and your interests.

Before putting together a resume or reaching out to alumni networks, you’ll need to apply to, get accepted by, and complete a degree program — this is where we can help. We’ve assembled not only great tips to keep in mind when researching programs, but program recommendations at the bachelor’s, master’s, and Ph.D. levels.

Interest in machine learning is at an all-time high as the technology becomes widely adopted by individuals and businesses alike. Get matched to the right machine learning education for you and begin a career in one of the most in-demand jobs over the next decade. Forward!