A stay-at-home parent scores a model’s responses while their baby naps nearby. In an office building across town, a data engineer finishes a new pipeline. On the other side of the world, digital nomad finds a quiet cafe to edit generated text into professional content.
Although wildly different in terms of skill level, education, experience, and interests, these people are all working toward the same goal: the advancement of artificial intelligence.
The term “artificial intelligence” has been around since the 1950s, but its explosion in recent years has led to the creation of new companies, job titles, skill sets, and opportunities. Whereas traditional data-related roles were often confined to a cubicle and required years of training, AI jobs can now be performed from the comfort of a coffeeshop.
Digitizing the workforce
According to Aura’s AI Job Trends Report, Machine Learning Engineer is currently the most in-demand role, followed closely by Computer Vision Engineer and Generative AI Engineer. These more “traditional” AI roles require related degrees and work experience, but many AI-related roles allow for non-linear, non-traditional paths and requirements.
Source: AI Job Trends 2025 (Aura.ai)
On some level, most roles across all industries involve some level of interaction with generative AI models. In a recent survey, McKinsey found that 88% of respondents who use AI-related tools on a daily basis aren’t in traditional tech roles. Everyone from cashiers and educators to nurses and warehouse staff use some level of AI within their roles.
Pearson, a long-standing education company, even boldly claimed that “one way or another, all jobs will become tech jobs” in their recent Skills Outlook study.
Are we in the Wild West era of AI?
Although AI and machine learning roles are growing at a staggering rate every year, the industry has somewhat of a Wild West feel to it—opportunities abound, but the rules are hazy. New AI jobs pop up every day, job descriptions lack standardization, and every industry is trying to get into the game.
Established roles, like Human Resources Specialist for example, usually have clearly defined job descriptions, requirements, skillsets, and pay grades. That’s simply not the case for AI roles because the industry lacks the standards that more traditional, established job titles follow.
In the AI workforce, an AI Trainer can mean both a Machine Learning Engineer with a master’s degree and a freelancer with zero tech knowledge. These two candidates would have vastly different skillsets, educational backgrounds, and experience, adding chaos and confusion to their job search.
This lack of standardization also creates confusion when it comes to pay. Taking the AI Trainer role as an example, a quick Google search shows that the average salary for an “AI Trainer” in 2025 is:
$73,094 per year, according to Glassdoor.
$131,490 per year, according to the University of San Diego.
$53,000 – $135,000 per year, according to FreelancerMap.
$13.46 to $63.70 per hour, according to ZipRecruiter.
Job requirements are all over the place as well. Some AI Trainer roles require an extensive list of specific skills, including deep knowledge of frameworks like TensorFlow and PyTorch, programming languages like Python, years of experience, and a data science degree. At the same time, you can find AI Trainer roles looking for problem-solving skills, a certain level of English, and grammar and writing skills.
Demystifying AI jobs
If you find yourself scrolling on LinkedIn and wondering what the difference between an AI Trainer, AI Tutor, and AI Engineer is, you’re not alone. Descriptions for AI jobs are often confusing, used interchangeably, and non-standardized.
Some of the most high demand job titles revolve around AI engineering, including:
Machine Learning Engineer: Designs, develops, and implements machine learning models to solve complex problems and improve systems. Requires a strong background in computer science, mathematics, and programming, often with experience in Python, TensorFlow, or PyTorch, and a degree in computer science or a related field.
AI Engineer: Develops AI-based solutions by designing, training, and deploying AI systems to address specific business or technical challenges. Requires expertise in machine learning, programming, data analysis, and AI tools, typically supported by a degree in computer science, AI, or engineering.
Natural Language Processing (NLP) Engineer: Focuses on building and optimizing systems that process, analyze, and generate human language for applications like chatbots or language translation. Requires a foundation in linguistics, programming, and machine learning, with experience in NLP libraries like spaCy or NLTK and a degree in computer science or related fields.
Prompt Engineer: Designs and refines prompts to optimize the behavior and output of AI language models in specific use cases. Requires strong analytical and communication skills, familiarity with AI tools and language models, and a background in computer science, linguistics, or relevant experience.
Another crucial part of AI development is data. Some of the most lucrative and commonly posted data-focused job titles include:
Data Scientist: Analyzes and interprets complex data to provide actionable insights and build predictive models for decision-making. Requires proficiency in statistics, programming, and machine learning, typically supported by a degree in data science, mathematics, or computer science.
Data Analyst: Collects, processes, and visualizes data to identify trends and help businesses make informed decisions. Requires strong analytical skills, proficiency in tools like Excel, SQL, and data visualization software, and a degree in data analytics, statistics, or a related field.
Data Engineer: Designs, builds, and maintains the infrastructure and pipelines that enable efficient data collection, storage, and processing. Requires expertise in database systems, programming, and cloud platforms, typically with a degree in data science or engineering.
AI Research Scientist: Conducts cutting-edge research to develop new AI algorithms, models, and technologies that advance the field. Requires advanced knowledge in machine learning, mathematics, and programming, often with a Ph.D. in AI, data science, or a related discipline.
Transitioning into an AI-related role is also possible on the business side of the workforce as well. Companies are adding AI experience into their Product Management, Program Management, Portfolio Management, and other management role job descriptions. Some companies even create niche titles like AI Finance Manager, geared toward experts with a deeper understanding of AI models.
Creatives are not exempt from the AI revolution either. Popular job titles in the creative space include:
AI Content Creator: Uses AI tools to generate, curate, and enhance digital content such as videos, images, articles, and social media posts. Requires creativity, familiarity with AI platforms like ChatGPT or MidJourney, and experience in content creation, often with a background in marketing, communications, or design.
AI Writer: Leverages AI tools to craft written content, such as blogs, articles, and copy, often refining and editing AI-generated drafts for quality. Requires strong writing and editing skills, knowledge of AI writing tools, and a background in journalism, communications, or a related field.
AI Artist: Creates digital art by utilizing AI-powered tools and software to generate or enhance visuals based on specific styles or prompts. Requires a mix of artistic skills, proficiency in AI art platforms like DALL·E or Stable Diffusion, and a background in art, design, or visual media.
The previously mentioned jobs tend to be stable, full-time, in-house positions within large corporations, start ups, or scientific/academic organizations. Sometimes, these employers also hire on a part-time, freelance, or project basis.
Emerging and niche roles such as Generative AI Engineer, AI Ethicist, and Computer Vision Engineer are another area of the AI landscape. These jobs generally require a blend of higher education, professional skills, and experience with a deep understanding of AI models, although job descriptions for these specialized roles tend to be highly unstandardized.
But what if you want to work with AI on your own terms? Whether you’re completing a degree, enjoying retirement or parenthood, or simply have an interest in AI, you can find opportunities that fit your needs.
Welcome to the gig economy
The gig economy has exploded in recent years—from Uber to UpWork, low-commitment, highly flexible opportunities are everywhere. In the AI gig economy, AI Trainers are often the most sought after roles. Trainers are also sometimes called Tutors, Prompt Engineers, and Subject Matter or Domain Experts.
Regardless of the title, these roles usually center around advancing AI models through conversation and feedback. The requirements for these roles vary widely based on the complexity of available projects.
Basic training roles are usually entry level and only require English proficiency and other soft skills like problem-solving and logic. More niche, specialized training projects may ask for higher degrees in specific fields or an intermediate knowledge of programming languages.
Many freelancing platforms also feature opportunities for AI Writers, Artists, and Content Creators. These opportunities might include a single project, on-going freelance work, internships, or talent pools.
A talent pool of experts
In the world of AI data generation, a talent pool refers to a group, collection, or community of experts, across various domains, who are qualified, ready, and available to contribute to AI related projects on a freelance basis.
Talent pools, like Mindrift’s platform, are an excellent way to make a difference in the field of AI without making a drastic career change.
Talent pool members usually have a formal education and expertise in specific domains, such as:
Linguistics
Law
Philosophy
Mathematics
Computer Engineering
Medicine
History
Some experts might contribute a few hours, days, or weeks to a specific project, while others pursue on-going or recurring projects. In exchange, they earn competitive pay for completed tasks, valuable work experience to add to their resumes, and access to a welcoming community of professionals.
Start your AI journey with Mindrift
At Mindrift, innovation meets opportunity.
We're a pioneering platform dedicated to advancing the field of AI through collaborative projects with domain experts. Our focus on Generative AI data creation offers a unique chance for freelancers to contribute to AI development from anywhere, at any time.
Experts in our talent pool are invited to contribute to projects within their domain of expertise. If you’re invited to a project, you’ll enjoy a range of diverse tasks, secure payments, and a welcoming community as you contribute to the future of AI.
Explore our talent pools to see where you fit in and apply today!
Article by
Mindrift Team
