The field of Artificial Intelligence (AI) is experiencing rapid expansion, creating a wealth of opportunities for skilled professionals. If you’re drawn to the cutting-edge world of AI and machine learning (ML), now is an opportune time to explore the diverse career paths available. For years, roles like machine learning engineers and data scientists have consistently ranked among the best jobs, and the U.S. Bureau of Labor Statistics projects a significant 26 percent growth in computer and information research roles between 2023 and 2033.
This surge in AI adoption, fueled by advancements in generative AI tools such as ChatGPT, has naturally led to increased curiosity about the specific jobs within this domain and how to secure them. This guide provides a detailed overview of artificial intelligence careers, highlighting the essential skills and educational background needed to thrive.
Top AI and Machine Learning Job Roles to Consider
To give you a clearer picture of the landscape, let’s delve into six key job roles within AI, including typical responsibilities and average salary expectations. For further insights, consider watching this insightful video featuring AI pioneer Andrew Ng, where he discusses essential roles for AI upskilling and integration.
1. Artificial Intelligence (AI) Engineer
AI Engineers are the architects of intelligent systems. They leverage AI and machine learning methodologies to design and develop applications and systems aimed at enhancing organizational efficiency. Their primary focus is on creating the infrastructure, tools, and processes that enable the practical application of AI to solve real-world challenges. By “training” algorithms with data, they empower these systems to learn, adapt, and improve performance. The contributions of AI engineers can be transformative for businesses, leading to reduced operational costs, increased productivity and profitability, and data-driven strategic recommendations.
Average Base Salary: $111,913 (Source: Glassdoor)
Further Reading: What Is an AI Engineer? (And How to Become One)
2. Machine Learning Engineer
Machine Learning Engineers are specialists dedicated to the research, construction, and design of the AI algorithms that power machine learning. They are also responsible for the maintenance and refinement of existing AI systems. Often working closely with data scientists, who develop the models for AI systems, machine learning engineers act as a crucial link within data science teams. Their work involves conducting experiments and rigorous testing, performing in-depth statistical analyses, and ultimately building and deploying robust machine learning systems.
Average Base Salary: $122,019 (Source: Glassdoor)
Further Reading: What Is a Machine Learning Engineer? (+ How to Get Started)
3. Data Engineer
Data Engineers are the unsung heroes who build and manage the essential data pipelines that fuel AI and analytics. They create systems to collect, manage, and transform raw data into actionable information, making it readily available for data scientists, business analysts, and other data professionals. By ensuring data accessibility and reliability, data engineers empower organizations to effectively evaluate performance, identify areas for optimization, and drive strategic decision-making. The field of data engineering is remarkably broad, with applications spanning virtually every industry imaginable.
Average Base Salary: $105,973 (Source: Glassdoor)
Further Reading: What Is a Data Engineer?: A Guide to This In-Demand Career
4. Robotics Engineer
Robotics Engineers are the innovators behind the automated world. They are responsible for the development of robotic applications across diverse sectors, including automotive, manufacturing, defense, and healthcare. Their work encompasses the entire lifecycle of a robot, from designing novel robotic products and assembling prototypes for rigorous testing, to overseeing production processes in manufacturing plants and monitoring real-world performance. Robotics engineering is a multidisciplinary field, blending principles of mechanical and electrical engineering with the power of computer science.
Average Base Salary: $112,163 (Source: Glassdoor)
Further Reading: Guide to a Robotics Engineering Career
5. Software Engineer
Software Engineers, often referred to as developers, are the creators of the digital world. They design, develop, and maintain software for a vast array of applications, from intricate computer games to complex network control systems. Utilizing programming languages, diverse platforms, and architectural frameworks, they bring digital solutions to life. A software engineer’s role can also extend to testing, refining, and maintaining software created by other engineers. If you possess strong analytical skills, enjoy problem-solving, and are passionate about enhancing digital systems, a career in software engineering can be exceptionally rewarding. Software engineers specializing in AI are particularly in demand as AI applications become more prevalent.
Average Base Salary: $117,604 (Source: Glassdoor)
Further Reading: Software Engineer Career Path: Overview, Jobs, and Pay
6. Data Scientist
Data Scientists are the strategic thinkers who unlock insights from complex data. They define the critical questions that organizations need to address and devise data-driven approaches to find answers. Often employing advanced statistical methods and machine learning techniques, they develop predictive models to forecast trends and outcomes. A data scientist may also leverage machine learning to enhance data quality or refine product offerings, playing a pivotal role in data-informed decision-making and innovation.
Average Base Salary: $116,946 (Source: Glassdoor)
Further Reading: What Is a Data Scientist? Salary, Skills, and How to Become One
Breaking into the AI Field: Key Steps
Gaining entry into the AI job market requires careful preparation and a strategic approach. Understanding the specific requirements and skills associated with your target role is crucial. While pathways can vary based on job type, level, and industry, here are common steps to consider:
1. Obtain Relevant Education
A solid educational foundation is often a prerequisite for AI roles. Many positions, particularly those beyond entry-level, typically require a bachelor’s degree or higher. While some entry-level roles may accept an associate’s degree or equivalent experience, a bachelor’s degree in computer science, mathematics, or a closely related field is widely favored. For more advanced roles and research-oriented positions, a Master’s or Ph.D. degree may be necessary.
Further Reading: What Can You Do with a Computer Science Degree? 10 In-Demand Fields
2. Develop Essential AI Skills
Beyond formal education, acquiring practical AI skills is paramount. If you already hold a degree in a related field, consider enrolling in specialized courses to gain the technical expertise needed to excel in AI. Even without a traditional degree, certifications can demonstrate your commitment to potential employers and showcase your investment in relevant skills. Consider exploring these AI certifications and certificate programs:
- MIT: Artificial Intelligence: Implications for Business Strategy
- USAII:
- Certified Artificial Intelligence Engineer
- Certified Artificial Intelligence Consultant
- Certified Artificial Intelligence Scientist
- ARTIBA: Artificial Intelligence Engineer
- IBM Applied AI Professional Certificate
- IBM AI Engineering Professional Certificate
- Google Advanced Data Analytics Professional Certificate
3. Target Entry-Level Opportunities
Once you’ve built a solid skillset and gained confidence in your abilities, begin your job search by targeting entry-level positions. Many roles, such as software engineer or developer, are excellent starting points and often explicitly labeled as “entry-level” or “junior” in job descriptions. Positions requiring less than three years of experience are generally suitable for those beginning their AI career journey.
If you encounter challenges in your initial job search, consider pursuing internships or engaging in freelance projects or hackathons to further refine your skills. These experiences provide valuable feedback and networking opportunities that can be highly beneficial in your future career progression.
Further Reading: 5 Tech Entry-Level Jobs: No Experience or Commute Required
Launch Your AI Career with Coursera
The AI field is dynamic and constantly evolving. Continuous learning is essential to stay at the forefront of this exciting domain. Coursera offers a range of courses designed to equip you with the skills needed to thrive in AI:
For a foundational understanding of AI: Explore Google’s AI Essentials course. This beginner-friendly program is designed to empower individuals across various roles and industries with fundamental AI skills to enhance workplace productivity.
To prepare for a career as an AI engineer: Enroll in the Microsoft AI & ML Engineering Professional Certificate. This comprehensive program will teach you to design and implement AI and machine learning infrastructure, master key machine learning algorithms, and develop AI-powered agents capable of autonomous problem-solving.
For degree-seeking individuals: Consider pursuing a Bachelor of Science in Computer Science from the University of London or a Master of Science in Data Science from the University of Colorado Boulder, both offered through Coursera.
