Artificial intelligence is starting to transform every industry imaginable, from manufacturing and e-commerce to healthcare and finance. Understandably, companies are eager to boost their AI operations to grow their bottom line, decrease time to scale, and become more competitive, offering lucrative salaries to freshly minted artificial intelligence engineers. By Glassdoor’s salary aggregated data from hundreds of thousands of shared compensation, the median artificial intelligence engineer salary in the US is $126,745 annually. A machine learning engineer stands to earn even more, with the median being $130,756 a year.
With so much money on the line, it’s no surprise that more and more young adults are entering bachelor’s and master’s programs in computer science, artificial intelligence, and machine learning. But school is one thing — the workforce another. What skills does an aspiring AI engineer or ML engineer need to master to ace their interviews and land a job?
In this article, we’ll lay out the must-have AI skills you need to succeed in the field, so you can do a self-audit to see if you already have what it takes or need to seek out more education before applying.
What’s an artificial intelligence engineer?
Before we begin, however, let’s clarify what we mean when we say artificial intelligence engineer or machine learning engineer.
An AI engineer leverages artificial intelligence to design, develop, and deploy solutions to problems that impact everyday people and everyday life. Artificial intelligence engineers might focus on one area of artificial intelligence — like computer vision to improve self-driving cars, natural language processing to improve auto-translators, or deep learning to improve investment strategy — or work as a generalist. Though you will find job postings for artificial intelligence engineers, often individuals with these responsibilities have titles like software developer, software engineer, AI specialist, or AI engineer.
An ML engineer designs, develops, and deploys machine learning models and maintains them once they are out in the world. Just like an AI engineer, an ML engineer might focus on one product or feature, especially at a larger company, or do a little bit of everything, all the while focusing more squarely on machine learning than an artificial intelligence engineer would.
As you might guess from reading the descriptions, there’s a significant overlap in skillset between an AI engineer and an ML engineer. The below skills are just as applicable to someone working in machine learning as someone working in artificial intelligence.
Must-Have Artificial Intelligence Skills
Perhaps the number one most necessary skill for an AI engineer is the ability to write code. Most AI professionals are fluent in at least one programming language, with R, Python, Java, and C++ being among the most popular.
In addition to being adept with programming languages, AI specialists usually also need to be comfortable working in a variety of machine learning programming environments and libraries, such as PyTorch, TensorFlow, and Pandas.
If programming allows an AI engineer to speak to computers, applied mathematics gives them something to say. Ultimately, the “intelligence” of artificial intelligence boils down to using mathematics to teach computers to produce meaningful and useful outputs from the inputs fed to them by their environments. The most important subdisciplines in mathematics for someone looking to build an AI skills arsenal are probability, statistics, and linear algebra.
Probability — in mathematics, the calculation of the likelihood that a particular event will occur — is crucial for writing an AI model because it allows you to grapple with the inherent uncertainty that enters an AI system due to incomplete training sets and the contingency of the real world.
Statistics — in mathematics, the collection, organization, and analysis of numerical data — is crucial for AI engineers who want to program an AI model to learn from existing data sets and predict future outcomes.
Linear Algebra — in mathematics, the use of linear equations and matrices to model natural and artificial phenomena and compute with these models — provides crucial computational and data management tools for building AI models. Linear algebra is particularly important when it comes to linear regression, computer vision, deep learning, and other analysis of big data sets.
Core Machine Learning Methods
If you are an AI engineer today, there is a high likelihood that you will work especially frequently in machine learning. Accordingly, the basic machine learning methods — supervised learning, unsupervised learning, and reinforcement learning — are crucial AI skills.
In supervised learning, labeled datasets where each piece of data is tagged and classified are used to train a machine learning algorithm to output correct information when given a particular input, with these algorithms essentially learning by example to build an ML model. If done correctly, we don’t notice one of the most frequent uses of supervised learning in our daily lives: spam filters.
In unsupervised learning, unlabeled data sets are used to train a machine learning algorithm. While supervised learning algorithms learn by example, unsupervised ML algorithms make sense of the data themselves by discovering patterns and relationships, forming clusters, and cutting through the data to get to the most important data points. Recently, a group of researchers detailed in Nature how they used unsupervised learning to distinguish groups of Alzheimer’s and Parkinson’s Disease patients based on their molecular make-up rather than on how the diseases were presenting.
Reinforcement learning differs from supervised and unsupervised learning in that it is explicitly goal-oriented, with machine learning algorithms written to behave in ways that will maximize a numerical reward as they engage with a complex environment, usually through things called Markov decision processes. Reinforcement learning is applied throughout industries, including in teaching cars how to drive themselves.
While not one of these key paradigms, another important method of machine learning is deep learning, where “artificial neural networks” (ANNs) of algorithms inspired by the synapses and neurons of the human brain are used for tasks in key areas of machine learning like natural language processing (NLP), computer vision, and robotic process automation (RPA). One notable application of deep learning comes in the form of the chatbots that e-commerce outfits are increasingly deploying as their first line of customer service.
A visualization of a neural network by MIT.
Core Artificial Intelligence Areas
To be a well-rounded candidate for an entry-level AI engineer position, you should ideally also have a working knowledge of several artificial intelligence areas among the following: natural language processing, computer vision, robotics, robotic process automation, and knowledge reasoning and representation.
Natural Language Processing
Borrowing from computer science, machine learning, and linguistics, researchers and engineers working in the subfield of natural language processing (NLP) are concerned with giving computers the ability to understand, and even employ, written and verbal language at a near-human level. In Artificial Intelligence: A Modern Approach, Russell and Norvig identify three goals of NLP: computers are being taught to understand and use human language 1) to communicate with humans, 2) to learn from the vast amount of information encoded in human language (like the internet), and 3) to advance the understanding of the language we’ve developed in disciplines like linguistics and cognitive science.
Current commercial applications of NLP include translation tools like Google Translate, email spam filters, personal assistants like Amazon’s Alexa, and accessibility tools like Youtube’s automatic captioning. Another notable application is Generative Pre-Trained Transformer 3 (GPT-3), a predictive language model developed by OpenAI. The GPT-3 model can produce convincingly human-like text when fed short prompts, an ability K Allado-McDowell harnesses in the Pharmako-AI, a novel co-written with GPT-3.
Computer Vision is an AI subfield with a predictable goal: to endow computers with the ability to perceive, assess, and act on visual stimuli at a level equal to, or even exceeding, human vision. This ability is essential if computers are to become even more active in the physical world in the coming years.
That said, computer vision is already impacting our lives every day. Perhaps the most ubiquitous example is Apple’s Face ID technology, a facial recognition system that precisely maps a user’s facial biometrics. Computer vision is essential for the development of autonomous vehicles, and augmented reality video games like PokémonGo, as well as for a host of industries such as manufacturing and agriculture. Together with NLP, computer vision also plays a role in tasks such as text digitization.
While robotics is a separate discipline unto itself, there is increasing overlap with AI as researchers look to introduce intelligent technologies into the physical world. To engage with the world around it, a robot relies on a combination of sensors and effectors. Sensors provide an input of environmental information, while effectors — robotic arms, legs, grippers, and the like — allow a robot informed by these inputs to produce physical outputs and effect change on the world around it.
It’s well-known that robotics and AI have been driving increasing automation in a host of industries, perhaps most notably manufacturing. In recent years, however, the idea of AI-driven robots living with us in our homes has become closer to being a reality through technologies like the iRobot autonomous vacuum.
Robotic Process Automation
Robotic process automation (RPA) concerns the automation of complex business processes to drive efficiency and productivity. Rather than robots active in the physical world, in RPA the robots are metaphorical “software bots” that can learn business processes (such as bank transactions) autonomously and then complete them automatically at scale.
While RPA is more difficult to discern as we go about our daily lives, it’s everywhere around us, streamlining supply chain management, executing predictive maintenance on manufacturing systems, and configuring account services for banks, telecoms, and many others.
Knowledge Representation and Reasoning
Knowledge representation and reasoning (KRR) is a subfield of artificial intelligence concerned with communicating or representing the world in a way that computers can understand. KRR requires skills in formal logic, semantics, and so-called Semantic Web technologies, which focus on making the internet machine-readable.
Overlapping significantly with areas like computer vision and NLP, KRR has utility for tasks such as computer-aided diagnosis and Apple’s SIRI personal assistant.
Artificial engineers spend their lives immersed in data. To be successful, they need to be adept with all aspects of the data pipeline, from data collection and preparation to data analysis to data visualization — and even data architecture and cloud computing.
Data Collection and Preparation
Though AI is powered by data, the right data is not always easy to come by, and even if it is, often it’s not immediately usable. The early stages of the data pipeline thus entail searching for relevant databases or brainstorming ways to collect useful data, then writing algorithms to extract the data points that will allow for the best analysis.
AI engineers generally need to be able to engage in broader data analytics using machine learning tools. At the small scale, this usually involves choosing — or writing — the appropriate algorithm to extract insights from a data set, while at the large scale AI engineers can develop complex machine learning models to perform this analysis.
Gleaning insights from data is useless unless you are also able to communicate these insights to various stakeholders in compelling ways. For AI engineers, this often means becoming adept with applications like Tableau to create beautiful, information-rich data visualizations that can support the story they want to tell about the technologies they are building.
Data architecture concerns not what is done to data at each stage of the data pipeline, but rather how this data pipeline is constructed in the first place: “the blueprint for data and the way it flows through data storage systems…foundational to data processing operations and artificial intelligence (AI) applications,” according to IBM. Some companies will staff a specific AI architect to design and maintain AI-specific data pipelines.
While not a “data skill” per se, it’s worth noting that AI engineers need to know their way around cloud computing services like Amazon Web Services (AWS). Especially when working with big data, cloud computing provides crucial and sustainable access to data storage and computing power — and many recruiters want to see experience with cloud computing on a candidate’s resume.
While much of the focus when applying for AI jobs is on technical skills, companies are increasingly seeking individuals with strengths in soft skills like critical thinking, leadership, and communication. While they need advanced math and programming skills, AI specialists also need to be able to think and communicate effectively to collaborate with peers, get buy-in from higher-ups, and manage those reporting to them.
The importance of soft skills should come as no surprise: the World Economic Forum’s list of Top 10 Skills of 2025 is heavy with them.
Image Source: World Economic Forum
It also makes sense: more people than ever before are learning to code, and so as core computer science skills become less of a differentiator between candidates, emphasis on soft skills as differentiators will amplify.
The takeaway? Those freshman writing and humanities distribution requirements might not be a waste of time, but rather your secret weapon in the final-round interview for your dream AI job.
As you can see, AI engineering requires an advanced, diverse skill set, one nearly impossible for an AI aspirant to develop on their own.
That’s where we come in: we’ve assembled guides for educational opportunities in artificial intelligence and machine learning to help you take the next steps toward your AI career, whatever your background. Find them here: