Nowadays, artificial intelligence is involved in different industries to make them better and more profitable. It helps to understand customer behavior and automize tasks which, in turn, increases efficiency and decreases costs. For this reason, more and more organizations are looking for people who specialize in this field. The number of AI job opportunities keeps growing month after month, indicating a sustained appetite for such talent, especially among larger companies that can afford researchers and specialists.
In this article, we will look at the career prospects and median salary for an AI professional, as well as what kind of job opportunities there are in AI. We will present several careers available, as well as the core skills needed to set yourself up for success in each one.
Table of Contents
- What Is the AI Job Outlook?
- What Is the Salary Range for AI Jobs?
- What Are the Best AI Jobs?
- Best Jobs in AI: Next Steps
What Is the AI Job Outlook?
With the technological advancements and growing needs for more automation, companies are looking to hire more and more skilled professionals that understand the underlying logic and the ethics of artificial intelligence, and deploy algorithms into the organizational workflow in order to drive business growth and innovation while adhering to best practices.
According to the Artificial Intelligence Index Report, AI jobs in the United States made up 0.90% of all postings in 2021, showing an increase in numbers from 2020. Moreover, the same report states that demand for AI skills has also gone up in other professions in the US – at 0,33% of all job postings, divided by clusters, AI roles rose 1.5 times more than in 2018.
What Is the Salary Range for AI Jobs?
Last year, O’Reilly estimated the median salary of data and AI professionals at $146,000 per year (based on 2,778 respondents in the U.S. and 284 in the U.K.), growing at an average of 2.25% annually.
At tech giants such as Google and Apple, job compensation can rise even higher – and that’s before you consider perks and benefits such as an ultra-flexible schedule and remote work.
What Are the Best AI Jobs?
There are several job opportunities you can pursue in AI depending on the domain, tasks, and responsibilities. However, it is worth mentioning that each career path might differ across the industries and companies you’re applying for.
For most of the AI jobs listed here, there are engineering and scientific pathways. The main difference is that scientists usually come from academic backgrounds and focus on developing new machine learning models and approaches. Meanwhile, engineers apply these algorithms to real-world applications and building the system that supports them.
Machine Learning Engineer / Scientist
Machine Learning Engineer
Machine learning engineers are technically proficient programmers that build AI systems (among other ML applications) leveraging huge datasets to generate and develop algorithms able to learn and make predictions.
In general, this role involves organizing data, executing tests and experiments, and generally optimizing processes to help develop strong performing machine learning systems. There is continuous demand and this position rarely remains vacant.
If you’d like to learn more about how you can become a Machine Learning Engineer, check out the 365 How to Become a Machine Learning Engineer guide.
Machine Learning Scientist
Machine learning scientists or researchers develop generic methods and algorithms that solve research problems depending on the industry. They usually need to keep up with the latest academic literature and be aware of how new developments perform in experiments. For this reason, they focus more on studying the algorithms before implementing a simpler approach.
Modern ML researchers often come from academia. They likely hold a PhD degree in machine learning and have good mathematical and research skills.
Deep Learning Engineer
Deep learning engineers carry out data engineering, modeling, and deployment tasks. These include:
- Defining requirements, collecting, labeling, inspecting, cleaning, augmenting, and moving data
- Training deep learning models, defining evaluation metrics, searching hyperparameters, and reading research papers
- Converting prototyped code into production code, setting up a cloud environment to deploy the model, or improving response times and saving bandwidth
This role requires knowledge of deep learning in addition to the standard ML engineer skillset. It focuses on applications, such as speech recognition, natural language processing, and computer vision. Additionally, you need to have expertise in certain domains like neural network architectures or fully connected networks, CNNs, and RNNs for visual or speech tasks.
Computer Vision Engineer / Scientist
Computer Vision Engineer
Computer vision engineers apply computer vision algorithms and work closely with object-oriented software to handle the processing and analysis of large data populations. This, in turn, supports the automation of predictive decision-making through visuals to solve real-world problems. Most computer vision engineer jobs will focus on app development, computer vision systems improvements, and writing algorithms.
These are a few general tasks that you will regularly carry out on the job:
- Create, test, debug, deploy, and maintain computer vision algorithms and hardware for different environments
- Develop automated vision algorithms, especially for robotics and autonomous hardware systems
- Gather and optimize analytics from computer vision algorithms to improve their performance
- Study real-world problems and propose solutions
- Build technical documentation for computer vision systems for end-users
- Manage large and small-scale computer vision projects, define project requirements, prepare budgets, and run technical development teams
This is a complex job that combines high levels of knowledge primarily in mathematics and computer science. If you’re interested in becoming a computer vision engineer, you’ll need to have a strong knowledge of calculus and linear algebra, as well as ML libraries and programming languages such as C++, Python, and Java.
Computer Vision Scientist
Computer vision scientists, on the other hand, focus on developing new algorithms for research problems or optimizing current techniques for new tasks and research areas. Their main responsibilities are to:
- Research, design, implement and evaluate novel computer vision algorithms
- Work on large-scale datasets, creating scalable, robust, and accurate computer vision systems in versatile application fields
- Actively disseminate research outcomes in premier conferences and journals
In order to become a computer vision scientist, you need a master’s degree or PhD in computer vision or machine learning, as well as strong theoretical and practical knowledge of deep learning and programming with Python or C++. Of course, having previously published your work in computer science journals will also help you land an AI job more easily.
NLP Engineer / Scientist
NLP Engineer
NLP Engineers are responsible for developing and maintaining natural language processing systems in order to analyze human language and extract meaning, determine intent, or perform other tasks. At this role, you may also work closely with computer programmers and software developers for the creation of artificial intelligence systems that directly mimic human behavior.
NLP engineers have a wide range of responsibilities, which can include:
- Designing NLP systems in a production environment
- Defining appropriate datasets for language learning
- Using effective text representations to transform natural language data into useful features
- Developing NLP systems according to requirements
- Training the developed model and running evaluation experiments
- Implementing the right algorithms and tools
- Performing statistical analysis to refine models
- Keeping up to date with news from the field of machine learning
- Maintaining NLP libraries and frameworks
- Monitoring the performance of existing applications and identifying potential problems
As an NLP engineer, you should have a strong understanding of text representation, semantic extraction techniques, data structures, and modeling. You must also be able to effectively design software architecture. And here’s a pro-tip from us: the ability to write robust and testable code will greatly boost your AI resume.
NLP Scientist
NLP scientists work on developing novel NLP algorithms and techniques to solve different problems such as information extraction, text summarization, natural language generation, automated question answering, and others depending on the research group or industrial domain you are working in. In addition, they will work with existing relevant literature to align, compare and contrast with the state-of-the-art techniques and algorithms.
Here are the essentials you’ll need if you’d like to become an NLP scientist:
- A PhD in Computer Science or a related field
- Hands-on experience in developing NLP solutions, demonstrated through scientific publications and/or successful product development
- Strong software prototyping experience in a research environment, with tangible programming experience in Python
Robotics Engineer / Scientist
Robotics Engineer
Robotics engineers handle the creation, assembly, and maintenance of robotics technologies. They help people carry out their jobs faster, more efficiently, and more safely. With expert knowledge of mechanics, electronics, and computer science, they create machines that aid people in various industries.
The overlap between AI and robotics lies in teaching the machine to do specific tasks, which requires implementing and deploying ML and computer vision algorithms. After it has been trained, it should be able to do perform tasks on it is own using reinforcement learning and deep reinforcement learning.
Several duties of a robotics engineer include researching robotic fields, such as AI interfaces, as well as the parameters of robotic application. You would also study the necessary components for proper robot functionalities, such as microprocessors. Essentially, all of this will lead to the design of robotic systems that work autonomously. You will be assembling, testing, and evaluating the effectiveness of your design throughout their lifecycle, and troubleshooting the prototypes where needed.
You need to develop your skills in three different areas before applying for a robotics engineer role:
- Advanced mathematics: algebra, and calculus
- Science and robotics comprehension, including geometry and physics
- AI and computer programming
All of these will help you better understand the abstract concepts of robotics and what part AI has to play in them.
Robotics Scientist
Robotics scientist positions can vary widely, however, every role incorporates heavy AI research. The goal is to help robots understand and autonomously perform their assigned tasks. To accomplish this, the research group develops novel decision-making methods to solve outstanding automation tasks.
As a robotics scientist, you will focus on the development of computational methods and algorithms for reinforcement learning, planning under uncertainty, and decision-making in multi-agent systems.
So, what are the requirements? Well, researchers in this field usually have a master’s or equivalent degree in disciplines such as Robotics, Computer Science, Machine Learning, Artificial Intelligence, Control Engineering, Mechatronics, and Applied Mathematics. On top of that, you should have strong experience in machine learning, as well as good programming and mathematics skills.
Data Scientist
Yes, even data scientists use AI! Essentially, these are professionals who specialize in collecting, wrangling, analyzing, and interpreting data. They use their skills to help organizations make better decisions, improve their operations, and promote a data-oriented culture.
There are no exact job definitions or role descriptions as it depends on the company, however, there are some common tasks that data scientists will likely be involved in:
- Data collection – acquiring all the necessary information, distributed across several databases, in order to complete a specific analytical task
- Data transformation – manipulating the data and measuring its quality in order to decide what assumptions can be made
- Data modeling – building a suitable model based on the task at hand and the nature of the collected data
- Data Reporting – sharing meaningful insights or deploying the model for production, depending on the task at hand
These are just generalized tasks, however, a typical day would vary across different business organizations.
Data scientists typically have a strong background in mathematics, statistics, and computer science. They use this knowledge to analyze large datasets and find trends or patterns. Additionally, they may develop new ways to collect and store data.
Applied Data Scientist
Applied data scientists carry out scientific research with a focus on using the results to solve real-world problems. They develop questions, then conduct studies that lead to practical solutions.
These scientists work in various industries and use their skills to develop functional applications in crucial areas such as medical research and engineering. More often, you’ll find them working for commercial organizations to find solutions that improve business operations.
AI Engineer
AI Engineers develop intelligence applications and systems to enhance the performance and efficiency of business processes. In this way, they help the company make better decisions, decrease costs, and increase revenue and profits. Simply put, they use software engineering and data science to streamline a business with automation.
Check out the 365 AI Applications for Business Success course to gain a better understanding of how AI is helping companies grow.
Some of the responsibilities of an AI engineer include:
- Coordinating with business leaders and software development teams to determine what business processes can be improved through the use of AI
- Developing the AI process and infrastructure
- Using machine learning techniques for image recognition
- Applying natural language processing techniques to text and voice transcripts to pull insights and analytics
- Building and maintaining chatbots that interact with customers
- Developing AI-driven solutions that mimic human behavior to automize repetitive tasks
- Building, training, and perfecting machine learning models
- Simplifying the ML process so that other business applications can interact with them using APIs
- Building recommendation engines for shopping sites, streaming services, and others
- Developing pipelines that structure data for AI processes
If you’re interested in becoming an AI engineer, you should have strong programming skills, a mathematical background (especially in linear algebra, probability, and statistics), work with big data technologies, and have hands-on experience in building machine and deep learning algorithms.
Best Jobs in AI: Next Steps
Artificial intelligence plays a very significant role in shaping the future of business and society. Its impact on different industries is only expected to increase in the coming years, as more data and computational resources become available.
If you’d like to get ahead in your AI career, start by building a well-rounded skillset. Most jobs will require more than just an understanding of AI, as we’ve seen, so it’s good practice to master data science fundamentals like statistics, and linear algebra, before progressing onto more specialized Machine Learning topics. The 365 line-up includes courses on the most popular ML algorithms today, walking you step by step through the intuition behind each model and providing many opportunities for applying your skills in practice. For starters you can explore linear regressions and the essentials of predictive modeling with our course Machine Learning in Python.
