Editorial Staff
Updated June 8, 2026
8 min read
Key Takeaways
- AI engineers translate AI and machine learning capabilities from lab settings into practical real-world systems.
- Common job titles include AI engineer, machine learning engineer, AI specialist, software engineer, and data scientist.
- Relevant hard skills include software engineering, programming, AI algorithms, machine learning models, and cloud or data tooling.
- Communication, cross-functional teamwork, subject-matter learning, and user feedback are central to human-centered AI engineering.
- Common preparation paths include computer science degrees, graduate AI study, and machine learning bootcamps.
The Field Is Exploding
Artificial intelligence is growing, and not just in the research labs of the world’s leading universities. Increasingly, businesses are harnessing the power of artificial intelligence and machine learning to optimize their operations and deliver better products to consumers. If you’ve ordered an Uber, browsed Netflix, or added something to your cart on Amazon, you’ve experienced first-hand the impact artificial intelligence can have on our daily lives.
In the coming years, this impact will only grow: according to Stanford University’s 2022 AI Index Report, private investment in artificial intelligence in 2021 doubled that of 2020, topping $90 billion worldwide.
To translate this investment of capital into the practical application of AI technologies, companies are increasingly turning to professionals working in the fledgling field of artificial intelligence engineering. Regardless of whether their job title is AI engineer, machine learning engineer, artificial intelligence specialist, or even software engineer, AI engineering professionals focus on ensuring that technical capabilities in the lab can be implemented in the real world.
This pursuit can be lucrative: Glassdoor pegs the median artificial intelligence engineer salary at $119,640 annually. For a machine learning engineer, the median salary climbs to $124,040. Compare these to the average annual salary in the US, which the U.S. Bureau of Labor Statistics estimated to be $58,260 in 2021, or the average annual salary for a web developer, which Indeed pegs at $71,707.
If helping to implement cutting-edge technology sounds like an exciting career opportunity, you’ve come to the right place. In this article, we’ll elaborate on what exactly artificial intelligence engineering is as well as some common career paths for AI engineers. We’ll also cover what skills are needed to succeed as an AI or machine learning engineer and how you can get started building and honing them in a bootcamp, online bachelor’s degree, or master's program.
— AIFwD Editorial Staff
What Is AI Engineering?
According to Carnegie Mellon University's Software Engineering Institute (SEI), AI engineering is “an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts.”
This goal of reconciling the virtual with the real means that AI engineering involves multiple disciplines, including, according to SEI, systems engineering, computer science, and human-centered design.
Human-Centered AI
Carnegie Mellon’s SEI believes AI engineers must ensure that the technologies they develop align with the values, behaviors, and goals of the humans that will use them.
Scalable AI
Acknowledging the time and expense it takes to develop AI systems, CMU’s SEI argues that such systems ought to be designed in ways that allow for scalability and application across different industries and use-cases.
Robust and Secure AI
With the knowledge that the lab is very different from the real world, CMU’s SEI prioritizes the development of safety mechanisms to ensure AI technologies will work as intended, regardless of the environment.
How Are AI Engineers Making a Difference?
Perhaps counterintuitively, many AI engineering jobs don't actually use the title “AI engineer.” More often, someone working to implement real-world AI solutions will have a job title like machine learning engineer, software engineer, AI specialist, or even data scientist.
To make this concrete, here are three real-world examples of how AI engineers at different career stages impact key industries.
AI Engineer — Computer Vision / Health Imaging
Covera Health
Key responsibilities
- Consulting on Amazon Web Services (AWS) platform optimization.
- Designing, developing, and shipping AI models to maintain and improve existing analytics.
- Communicating results to internal and external stakeholders.
Requirements
- Bachelor’s or master’s degree in computer science or nearby field.
- 1–3 years of experience, including internships, as a software or AI engineer.
- Experience with SQL, a programming language like Python or C++, and Unix environments.
- Experience with machine learning models, deep learning pipelines and, ideally, deep learning environments inference engines like TritonRT.
Machine Learning Engineer — Manufacturing
Apple
Key responsibilities
- Writing ML algorithms to improve Apple’s supply chain and manufacturing processes.
- Designing, developing, and deploying machine learning systems to Apple’s factories worldwide.
- Collaborating with software engineers, hardware engineers, machine learning engineers, and operations teams.
Requirements
- Master’s or PhD in Computer Science or related STEM field.
- Advanced hands-on experience writing machine learning algorithms and developing machine learning models.
- Experience with common ML tools like PyTorch, Apache Spark, TensorFlow.
- Advanced experience in a programming language like Python or R.
- Previous experience developing ML systems in manufacturing settings a plus.
Software Development Engineer — AI Natural Language
Amazon
Key responsibilities
- Collaborating with machine learning scientists on overall conversational AI strategy.
- Designing, developing, and shipping new AI models to improve Alexa’s capabilities.
- Improving system architecture and best practices.
Requirements
- Bachelor’s degree in computer science or electrical engineering.
- Facility with programming languages, including Python, C++, and similar languages.
- Experience with system architectures, software infrastructure, and cloud software services.
- Experience with AI systems a plus.
- Ability to successfully communicate with users and other teams or stakeholders.
What Skills Do You Need?
Across the jobs we examined — including diagnostics at Covera, manufacturing efficiency at Apple, and conversational AI at Amazon — there are striking similarities in the kinds of skills needed.
Hard skills
- Software engineering fundamentals
- Programming fluency in at least one language, and usually more
- Experience writing AI algorithms
- Experience developing AI and machine learning models
- SQL, Python, C++, R, and Unix environments
- Machine learning tools such as PyTorch, Apache Spark, TensorFlow, and deep learning pipelines
Soft skills
- Cross-functional communication
- Explaining complex ideas to non-technical stakeholders
- Collaborating with product managers, data scientists, and users
- Developing subject-matter knowledge
- Engaging with user feedback to identify pain points
- Leading teams of other engineers
AI engineering isn't just about building impressive technologies, but ensuring these technologies are designed with humans at the forefront. A typical meeting could include a product manager, a data scientist, and non-technical stakeholders.
How Can You Develop These Skills?
The most common way into AI engineering is through a bachelor's degree in computer science with an AI or ML specialization. For most entry-level roles, this gives you sufficient training in software engineering fundamentals, programming, and the tools needed to write AI algorithms.
Some senior roles — like the Apple example above — require a master's or PhD. Graduate study deepens your expertise in areas like deep learning, computer vision, and robotics, and unlocks roles with deeper compensation and higher impact.
But a degree isn't the only path. Increasingly, students and career-changers opt for artificial intelligence, data science, or machine learning bootcamps as a faster, less expensive way in.
4 years
Bachelor’s Degree
A computer science bachelor’s degree can provide software engineering, programming, AI, and machine learning foundations.
Browse programs →1–3 years
Master’s Degree
Graduate study in AI or machine learning can deepen knowledge in deep learning, computer vision, and robotics.
Browse programs →6+ years
PhD
A PhD can support research-heavy AI roles and advanced applied science opportunities.
Browse programs →3–6 months
Bootcamp
AI, data science, or machine learning bootcamps can offer a more flexible, project-based entry point.
Browse programs →Top AI / ML Programs Compared
Updated Jun 2026| Program | Cost | Format | Duration | Rating | |
|---|---|---|---|---|---|
| Southern New Hampshire University | Request info | Online | Varies | Program details | View → |
| Syracuse University | Request info | Online | Varies | Program details | View → |
| Wake Forest University | Request info | Online | Varies | Program details | View → |
| University of California, Berkeley | Request info | Online | Varies | Program details | View → |
Frequently Asked Questions
What is artificial intelligence engineering?+
Carnegie Mellon University’s Software Engineering Institute describes AI engineering as an emergent discipline focused on developing tools, systems, and processes that enable artificial intelligence in real-world contexts.
What job titles overlap with AI engineer?+
Many people doing AI engineering work have titles such as machine learning engineer, software engineer, AI specialist, data scientist, robotics engineer, big data engineer, or business intelligence developer.
What skills do you need to work as an AI engineer?+
AI engineers typically need software engineering fundamentals, programming fluency, and experience writing AI algorithms and developing AI and machine learning models. Communication, teamwork, subject-matter learning, and user feedback skills are also important.
Do you need a graduate degree to become an AI engineer?+
Not always. A bachelor’s degree in computer science can be enough for many entry-level roles, but some employers and more advanced roles prefer a master’s degree in artificial intelligence or a PhD in artificial intelligence.
Can a bootcamp help you start an AI engineering career?+
A degree program can help, but it is not the only route. Artificial intelligence, data science, and machine learning bootcamps can be flexible, less expensive ways to build practical skills and portfolio projects.
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