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Artificial intelligence (AI) is one of the fastest-growing fields in the world today, offering exciting career opportunities for those who want to shape the future of technology.
Today, we explore the steps to get a job in AI, the skills you need to develop, how to optimize your search for AI positions, and also how to start a career in AI without a degree.
Learn how to get a job in AI and what multi-billion dollar companies like Tesla, OpenAI, and Pfizer are asking of their AI job applicants.
Key Takeaways
- AI jobs offer opportunities across industries like automotive, healthcare, and retail.
- Essential AI skills include machine learning, deep learning, and computer vision.
- You don’t need a degree to start in AI — self-learning can be effective.
- Top companies hiring in AI include Tesla, OpenAI, and Pfizer.
- AI ethics roles are emerging to guide responsible technology development.
How to Get a Job in AI
There are many routes you can take to get a job within the AI industry. The most important thing is that you acquire the technical knowledge and skills necessary to create and manage AI solutions.
This knowledge can be acquired via a university course, such as a computer science degree, or via self-learning software engineering with the help of online courses and books.
While a formal educational background isn’t a prerequisite for a job in AI, getting a computer science degree or equivalent qualification can give you an edge over other applications, as recruiters will often filter applications based on whether they have a bachelor’s or master’s degree in computer science.
Skills Involved in AI Jobs: Terms You Need to Know
Whether you’ve recently graduated, switching from another career, or pivoting to a new role, it is crucial to have a solid grasp of technical skills and terms used across AI:
Machine Learning
Machine learning (ML) is a data science field that involves training algorithms to imitate how humans learn to make autonomous, accurate decisions. ML models are one of the core building blocks of AI systems, so it’s essential you have a solid understanding of these if you want to thrive in the industry.
Deep Learning
Deep learning and ML are two terms that are often used interchangeably, but they are fundamentally different approaches.
Deep learning is where AI is used to process a dataset and extract data without human intervention, while an ML model needs to be actively trained by a researcher. A deep learning model can specifically learn from previous mistakes autonomously.
Artificial Neural Networks
Artificial Neural Networks are designed to emulate the human brain, featuring networks of nodes that are structured similarly to neurons.
These networks can be fine-tuned to perform tasks such as image and speech recognition. One of the most well-known examples of an ANN is Google’s search algorithm.
Natural Language Processing
Natural language processing (NLP) enables computers to interpret text and speech and is the foundation for many digital technologies, including virtual assistants like ChatGPT, Siri, and Alexa, plus voice-operated GPS systems, speech-to-text dictations, and more.
Computer Vision
Computer vision aims to enable computers to understand images and video, allowing machines to interpret the world around them. Computer vision technology is used to develop robots, surveillance cameras, identification systems, text extraction tools, augmented reality (AR), autonomous vehicles, manufacturing industries, and more.
Data Analysis
Data analysis is the process of collecting and analyzing data sets to spot patterns and trends. Data analysis skills are essential for building data analytics solutions and extracting insights from datasets, which help aid real-world decision-making and problem-solving.
Big Data
Big data refers to analytics techniques used to study large datasets of structured, semi-structured, and unstructured data taken from multiple sources. Knowing how to integrate and analyze big data is essential for unlocking insights into how to make a business more operationally efficient.
Business Intelligence
Business Intelligence involves collecting, managing, and processing data to develop insights into an organization’s operations. This often involves illustrating trends via graphical displays such as reports, charts, and graphs.
Coding Languages
At a minimum, a knowledge of computer programming languages like Python, C++, and Java — if you wish to be involved in the building of AI — is going to be an advantage.
Job Hunting for an AI Career
Now you understand how to get a job in artificial intelligence, we’re going to examine how to apply for your next role. When looking for an AI-related position, there are a number of tips you can follow to increase your chances of success:
- Search for job listings and posts on social platforms like LinkedIn
- Browse the Careers page of potential recruiter companies
- Keep your resume up-to-date with the latest education and work experience information
- Write a carefully crafted cover letter with each application
- Don’t rule out internships – these can be a great entry point to gain experience and work towards landing a full-time position
- Use online courses and self-learning to make sure your skills continue to evolve
- Start networking and reach out to friends, peers, and colleagues in your field
- Participate in social activities like hackathons and seminars
- Try to build an online presence in your field by producing relevant content that demonstrates your knowledge and expertise
AI Jobs in Various Industries
If you want to learn how to get a job in AI, then you need to have a good idea of how it’s shaping the world today. That means a solid understanding of how major industries like automotive manufacturing and healthcare are using technology to develop new products.
Developing an understanding of how AI is being used in your chosen industry will help inform the skills you need to develop to find a relevant position in the sector.
Automotive
AI has helped revolutionize the automotive industry and disrupted everything from product design to consumer financing. Right now, we’re starting to see more and more manufacturers experimenting with AI to develop autonomous vehicles that can operate without a human driver.
Tesla, for instance, is experimenting with AI as part of its Robotaxi, an autonomous vehicle that can operate without human intervention. These technologies depend on AI and ML for activities, including object identification, prediction modeling, speed regulation, and speech recognition.
We’re also seeing AI transform the manufacturing process, with companies like Autodesk testing ways to design cars using AI while big data is used to analyze and optimize supply chain networks and production lines in the background.
Healthcare
The health industry is another hub of AI development, home to many biotech startups, healthcare providers, and pharmaceutical companies that are using AI to enhance patient care with streamlined drug development, robot-assisted surgery, preliminary diagnosis, remote consulting, patient monitoring, and automated symptom checkers.
One of the biggest trends we’re seeing in the medical industry is the use of wearables, which healthcare providers have been using to monitor patient vitals like heart rate to help assist in the early detection and diagnosis of life-threatening conditions such as heart disease and cancer.
Banking and Finance
Financial institutions have also jumped on the AI bandwagon, using the technology to support market analysis, personalized product offerings, risk mitigation, fraud detection, and customer service.
Key players in the industry, from JP Morgan Chase to Capital One and Wells Fargo, have all been investing in growing their AI talent pools to try and increase efficiency.
Fraud detection and cybersecurity are becoming a major focus area in the sector, as online fraud continues to grow with hackers using low-hanging fruit techniques like phishing scams to steal customer’s payment details.
Retail
Retail and e-commerce providers are also using AI to help out with inventory management, marketing outreach, demand forecasting, customer engagement, data analytics, and supply chain management.
With the global AI in the retail market expected to grow from $9.36 billion in 2024 to $85.07 billion by 2032, brushing up on your data analytics and business intelligence expertise can be a lucrative decision, as many retailers are constantly looking for ways to extract more insights from their data.
Real-World Examples of AI Jobs
To help you get an idea of what jobs your AI skills open the door to, we’ve put together a brief list of some of the Artificial Intelligence job opportunities the market has to offer.
You may notice that many of these positions mention experience, skills, and technical knowledge over a degree – suggesting that there is some flexibility in AI job requirements if you have the right ability.
Automotive: Tesla – AI Research Engineer, Self-Driving
As of November 2024, Tesla is seeking an AI research engineer to help analyze the largest self-driving dataset in the world. The requirements are as follows:
- Proven experience in scaling and optimizing large AI models
- Proficiency in Python and an understanding of software engineering best practices
- In-depth knowledge of deep learning fundamentals (optimization techniques, loss functions, and neural network architectures)
- Experience with deep learning frameworks like PyTorch, TensorFlow, or JAX
The salary is $140,000 – $360,000, along with cash and stock awards and benefits.
Generative AI: OpenAI – Machine Learning Engineer, Retrieval & Search
OpenAI is seeking a machine learning engineer to manage retrieval and search problems across its API and ChatGPT. The requirements are as follows:
- Extensive experience building and maintaining production machine learning systems
- Prior experience working with vector databases, search indices, and other data stores
- Prior experience building and iterating on internet-scale search systems
The salary, as listed in November 2024, is between $310,000 and $550,000.
Healthcare: Pfizer – AI/ML Product Engineer
Pfizer has been seeking a (now-filled) role for an AI/ML Product Engineer to help create new enterprise AI products. The requirements were as follows:
- A degree in data science, computer science, informatics, life sciences, physics, statistics, or another related field
- 5 years experience as a data scientist, machine learning or NLP engineer
- 5 years of experience working with structured, semi-structured, and unstructured data
- Experience with NLP libraries and technologies (including Space, PyTorch, and Deep Learning models)
- Knowledge of Python, SQL, Scala, or Java
The salary was between $93,000 and $155,900.
An Alternative Route: AI Ethics
In the event you don’t want to focus on building and deploying AI systems yourself, you can also look to move toward AI ethics.
AI ethics is a discipline that’s about exploring the moral, ethical, and legal implications of AI development. AI ethicists play a critical role in advocating for responsible AI development in an organization and making sure that new solutions are designed safely and morally.
For example, an AI ethicist may be tasked with finding ways to mitigate machine bias and prejudice in AI models or by making sure that customer data is processed securely, in compliance with legislation such as the GDPR.
Many companies are hiring AI ethicists to help set guidelines on how to develop AI systems responsibly and minimize risk to their customers and society at large, so this a field that’s worth paying attention to.
The Bottom Line
With the right education, skills, and determination, getting a job in AI is an achievable goal. While having a formal education in computer science can provide an advantage over other applicants, practical experience and knowledge can still take you very far.
Taking the time to self-learn, participate in coding boot camps, and hone your real-world experience through internships can help you to steadily build a portfolio of valuable knowledge and experience, which you can share with potential employers.
AI is a dynamic and rapidly evolving field, so it’s vital that you continue to improve your skills, or you risk falling behind.
Keeping up-to-date with the latest developments and re-skilling will ensure that you stay at the top of your game.
