Machine learning engineers — advanced programmers who develop artificial intelligence (AI) machines and systems that can learn and apply knowledge — are in high demand, as more companies adopt these technologies. ML engineers perform sophisticated programming and work with complex data sets and algorithms to train intelligent systems.
While many fear that AI will soon replace jobs, at this phase in the technology’s development, it is still creating positions like the machine learning engineer, as companies need highly skilled workers to develop and maintain a wide range of applications.
To help those interested in the field better understand how to break into a career in machine learning, we compiled this collection of useful details and resources. We’ll regularly update this guide on how to become a machine learning engineer.
What is machine learning?
Machine learning is a branch of AI that enables computer systems to automatically learn and improve from experience, rather than being explicitly programmed. In machine learning, computers use massive sets of data and apply algorithms to train on and make predictions.
Machine learning systems can rapidly apply knowledge and training from large data sets to perform facial recognition, speech recognition, object recognition, translation and many other tasks.
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Why is there increased demand for machine learning engineers?
Demand for AI talent, including machine learning engineers, is exploding, as organizations of all sizes are finding ways to leverage the evolving technologies. Companies are increasingly adopting AI and machine learning solutions “to enhance the customer experience, increase sales and improve operations,” according to WSJ Pro.
In its Artificial Intelligence Transformation Report, App development firm Reign found that “Over the past decade, the U.S. has seen a 21% increase in the number of AI jobs,” adding that AI-related job wages increased by 27% between 2010 and 2020. And ITCareerFinder recently reported that jobs requiring AI/machine learning skills are expected to grow 71% over the next five years.
What is driving the growth of the machine learning market? Fortune Business Insights attributes the increase to AI adoption in a host of industries, including healthcare, retail and manufacturing. The COVID-19 pandemic has also contributed to the rise in AI/ML usage, as the technologies are being used to track, trace and predict COVID cases, as well as help organizations implement automation strategies to bounce back from the impact of the pandemic.
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What are some machine learning engineer job roles?
Machine learning engineers can take a variety of career paths. Here are a few roles in the field, and the skills they require, according to Udacity.
- Software engineer, machine learning: Computer science fundamentals and programming, and software engineering and system design
- Applied machine learning engineer: Computer science fundamentals and programming, applying machine learning algorithms and libraries
- Core machine learning engineer: Computer science fundamentals and programming, applying machine learning algorithms and libraries, data modeling, and evaluation
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What programming languages are best to learn to become a machine learning engineer?
Python, R and Java/JavaScript top the list of popular programming languages, according to Springboard, followed by Julia and LISP. Perennial frontrunner Python — which GeeksforGeeks says is favored by 60% of machine learning developers — is flexible, scalable and easier to learn than many other languages.
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What other skills are required to become a machine learning engineer?
Generally, machine learning engineers must be skilled in computer science and programming, mathematics and statistics, data science, deep learning and problem solving. Here is a breakdown of some of the skills needed, according to Udacity.
Computer science fundamentals and programming: Data structures (stacks, queues, multi-dimensional arrays, trees, graphs), algorithms (searching, sorting, optimization, dynamic programming), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing).
Probability and statistics: Formal characterization of probability (conditional probability, Bayes’ rule, likelihood, independence) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models). Statistics measures (mean, median, variance), distributions (uniform, normal, binomial, Poisson), and analysis methods (ANOVA, hypothesis testing).
Data modeling and evaluation: Finding patterns (correlations, clusters, eigenvectors), predicting properties of previously unseen instances (classification, regression, anomaly detection), and determining the right accuracy/error measure (e.g., log-loss for classification, or sum-of-squared-errors for regression) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation).
Applying machine learning algorithms and libraries: Standard implementations of machine learning algorithms are available through libraries, packages, and APIs (such as scikit-learn, Theano, Spark MLlib, H2O, and TensorFlow). Applying them effectively means selecting the right model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models) and a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning.
Software engineering and system design: Machine engineers are typically working on software that fits into a larger ecosystem of products and services. That means they need to understand how the different parts work together, communicate with the parts (using library calls, REST APIs, and database queries), and build interfaces for your piece that others can use. This involves knowing system design and software engineering best practices (including requirements analysis, system design, modularity, version control, testing, and documentation).
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What is the average machine learning engineer salary?
Machine learning engineers in the US earn an average salary of $131,255, according to the latest data from Indeed. However, ZipRecruiter cites a higher national average of $157,676 ($76 an hour) as of March 28, 2022.
ZipRecruiter also lists the 10 US cities with the highest average salaries. Topping the list are Richmond, California ($192,929), Stamford, Connecticut ($187,866), and Bellevue, Washington ($187,441).
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Where are the hottest markets for AI and machine learning engineer jobs?
The San Francisco Bay Area, Austin, New York City and Denver are listed by Datamation as tech hubs featuring high concentrations of AI and machine learning jobs. And according to the most recent CompTIA Tech Jobs Report, AI positions, which accounted for roughly 20% of all emerging tech job postings, were most prevalent in California, Texas, Massachusetts, Florida and Washington.
Which industries are posting these jobs? Dice identifies professional, scientific and technical services; finance and insurance; and manufacturing as the top hiring fields.
What are some typical machine learning engineer interview questions?
Those applying for machine learning jobs can expect a number of different types of questions during an interview, testing their skills in mathematics and statistics, data science, deep learning, programming, and problem solving.
Some questions that a machine learning engineer can expect to be asked during an interview include:
- What have you been working on for the past few years?
- What AI and machine learning tools are you familiar with, and how proficient are you in them?
- What do you do to stay on top of changing technologies?
- How do you clean and prepare data to ensure quality and relevance?
- How do you handle missing or corrupted data in a dataset?
- What are the ethical implications of using machine learning?
It’s also important for the job applicant to arrive at the interview with questions for the hiring manager, Dave Castillo, managing vice president of machine learning at Capital One told TechRepublic.
“An interview is a two-way conversation,” Castillo said. “Just as important as the questions that we ask are the questions that candidates ask us. We want to ensure that not only is the candidate the right choice for the company, but the company is the right choice for the candidate.”
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Where can I find resources for a career in machine learning?
There are many paths into a career as a machine learning engineer. A good place to start is by mastering a programming language like Python, R or Java. For machine learning specifics, a number of Massive Open Online Courses (MOOCs), online programs and certifications are available, including classes on Coursera and edX, and a nanodegree from Udacity. Special deals and training bundles are available from TechRepublic Academy, as well.
You can also gain practical experience through doing real projects on real data, on sites like Kaggle. Joining local organizations such as meetups or hackathons to learn from others in the field can also help.
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Source: TechRepublic
