14 best entry-level AI jobs: A beginner’s guide

Artificial intelligence (AI) is changing the way people work, and it’s also creating new types of jobs.

A few years ago, most AI careers required advanced technical degrees or years of engineering experience. That’s no longer the case.

Today, many entry-level AI jobs are open to beginners, including remote and freelance positions focused on data labeling, prompt testing, research, content review, and AI product support.

While some technical roles require coding or data skills, many AI jobs prioritize strong communication, attention to detail, critical thinking, and the ability to learn quickly.

Below are some of the popular entry-level AI jobs to explore in 2026, along with salary estimates, skill requirements, and how to get started.

14 best entry-level AI jobs for beginners in 2026

Entry-level AI job opportunities generally fall into two main groups. Some roles are non-technical, requiring little to no coding experience. Others are more technical and require skills in programming, data analysis, or machine learning, but they may also offer higher long-term earning potential.

Compensation varies depending on the role, specialization, and experience level. We’ve provided some estimates based on publicly available online research.

Non-technical, often freelance and remote roles

The following roles offer some of the fastest entry points into AI. Most are remote and freelance-friendly and focus on tasks like evaluation, testing, or feedback rather than building models.

1. AI data annotator

AI systems rely on labeled data to learn patterns and improve performance. Data annotators help by tagging images, text, video, or audio so models can better understand information.

This is one of the most accessible entry-level AI jobs because it typically doesn’t require technical experience. Strong performance can lead to more advanced AI training roles that involve deeper evaluation and feedback.

Best for: Detail-oriented beginners, remote freelancers, flexible work seekers

2. AI trainer / RLHF specialist

AI trainers evaluate model outputs and provide structured feedback that improves response quality and accuracy. This remote work goes beyond basic annotations and some projects require domain expertise in areas such as writing, law, medicine, or finance.

Typical pay starts from $15 per hour and can exceed $60 in some cases, depending on specialization and role availability. Entry-level AI training jobs provide direct experience working with production AI systems.

Best for: Expert professionals with strong communication or domain knowledge

3. Prompt engineer

Prompt engineering is a legitimate role that involves far more than simply interacting with an LLM like ChatGPT.

Prompt engineers design and refine prompts that help AI systems generate better results. This role involves testing workflows, documenting, and refining prompts for real use cases.

Often, AI jobs for beginners in this area require a portfolio rather than formal credentials. As one of the more competitive roles in this group, according to Coursera, Ziprecruiter, and Glassdoor, average prompt engineer rates can range from $30 to $60 or more per hour.

Best for: Writers, analytical thinkers, AI enthusiasts

4. AI content specialist

These roles involve reviewing AI-generated content for accuracy, tone, and bias. Professionals from writing or editing backgrounds often transition into these AI jobs without a degree, and the work can lead to more advanced evaluation projects over time.

Best for: Writers, editors, QA professionals

5. Search engine evaluator

Search engine evaluators assess the relevance and quality of results generated by AI systems. The role requires attention to detail and strong judgment rather than technical skills. Many AI jobs with no experience involve this type of work, and roles are often flexible and remote.

Best for: Beginners looking for flexible remote work

6. Junior QA or product tester

Product testing and junior QA roles focus on testing AI features before their release. Tasks include identifying bugs, reviewing outputs, and documenting issues for development teams. Over time, this path can lead to product management or specialized AI testing roles.

Best for: Problem-solvers with strong attention to detail

7. Customer support for AI-powered services

AI-powered platforms require support professionals who understand how these systems behave in real-world use cases. This work involves troubleshooting, guiding users, and collecting feedback for improvement. The role can provide exposure to real-world AI applications and workflows.

According to Indeed, as of May 2026, the average customer support pay is about $19 per hour in the United States (based on data from job postings on Indeed in the past 36 months).

Best for: Customer-facing professionals and communicators

8. UX feedback on AI-driven tools

UX-focused roles involve testing usability and documenting user experience with AI tools. Professionals evaluate how systems perform across different scenarios and provide structured feedback. Strong UX insight can also lead to product design or research roles.

Best for: UX-minded professionals and testers

9. Freelance research and editing

AI systems frequently rely on high-quality research and editing to improve outputs. Professionals in these roles verify facts, refine language, and ensure accuracy.

You can often get these AI jobs without a degree, and job sites like Ziprecruiter suggest the median pay ranges anywhere from $30 to $50 per hour (location matters too). The work directly supports training and improving AI systems.

Best for: Researchers, editors, writers

Technical, full-time roles with remote work options

In contrast to the above options, these positions require coding or data skills. They are often more competitive, but they may also offer higher salaries and long-term career growth.

10. Junior machine learning engineer / intern

Entry-level AI engineer jobs in machine learning (ML) involve assisting with data pipelines, model testing, and experimentation. Daily work may include debugging models and preparing datasets. These entry-level jobs are competitive but offer strong long-term growth opportunities.

Best for: Candidates with programming and machine learning foundations

11. Junior or associate data scientist (AI-focused)

Data scientists analyze datasets and build predictive models. Entry-level work focuses on cleaning data, running experiments, and interpreting results. These AI/ML entry-level jobs require knowledge of statistics and programming.

Best for: Analytical candidates with technical/data backgrounds

12. Junior NLP / generative AI analyst

These roles focus on language-based AI systems. Professionals help evaluate outputs, build datasets, and test generative models.

Generative AI jobs at the entry level often include prompt testing and data preparation. Research by Coursera suggests entry level compensation (0-1 years) generally starts around $91,000 per year.

Best for: Candidates interested in language models and generative AI

13. Junior AI/ML software engineer

This role involves integrating AI models into applications and building supporting systems. Daily work includes API integration, feature development, and performance optimization. Entry-level AI developer jobs require programming and system design knowledge.

Best for: Candidates interested in language models and generative AI

14. Entry-level AI developer (AI-driven systems)

AI developers build applications powered by AI tools and models. Tasks include integrating APIs, optimizing workflows, and improving system performance. The roles offer strong long-term career growth.

Best for: Developers interested in AI-powered products and applications

Entry-level AI jobs pay ranges

Entry-level AI work can pay hourly, per project, or through a full-time salary. Non-technical and freelance opportunities commonly use hourly or project-based compensation. Meanwhile, technical roles are more likely to be full-time positions with longer-term earning potential.

Actual rates depend on the role, your specialization, and any domain expertise you bring. Here’s how the publicly sourced pay figures on this page compare:

What type of entry-level AI jobs include training and onboarding?

Many entry-level roles, including data annotation, AI evaluation, and RLHF work, often include structured training and project-specific onboarding. This practice is especially common on dedicated AI-training platforms such as Mercor.

Mercor also matches candidates with opportunities based on their professional backgrounds, which can make it easier to transition into AI-related work without prior AI experience.

Onboarding typically covers task guidelines, quality standards, and the tools used for the project, giving contributors the context and support they need to get started confidently.

More broadly, entry-level roles in customer support, sales, operations, technology, healthcare administration, and skilled trades often include formal training or supervised onboarding. The level of support varies by employer, but these roles are generally designed to help new employees build relevant skills as they work.

How to find legitimate, entry-level AI projects that are flexible and remote?

A few signals can help you distinguish legitimate and flexible entry-level AI projects from low-quality listings:

• Transparent pay: Look for a stated hourly rate, per-project rate, or salary range, rather than vague "competitive pay."
• Clear scope: Confirm that the listing defines specific tasks, such as annotation, evaluation, or testing, instead of promising generic “AI work from home.”
• A verifiable platform or company: Credible opportunities usually originate from an established marketplace, employer, or AI lab with a track record you can research.
• No upfront fees: Avoid AI jobs that require payment before you can begin working.
• Real onboarding and support: Clear guidelines, quality feedback, and a point of contact suggest that the work is structured and legitimate.

Mercor and other specialized platforms connect candidates with AI companies hiring for entry-level and project-based roles, including RLHF and AI-training work. The services can provide access to vetted, paid opportunities.

General freelance marketplaces and AI-focused job boards can also list beginner-friendly roles. Review each listing carefully before investing time in an application.

What skills do you need for entry-level AI roles?

The requirements for entry-level AI jobs vary by role type and the path you choose. There’s no single fixed standard.

Non-technical roles

These positions focus on evaluation, content review, and feedback. Requirements frequently emphasize judgment and communication, attention to detail, critical thinking, and consistency.

Semi-technical roles

These roles require basic technical literacy, including prompt design, spreadsheet analysis, and light scripting.

They are well-suited to professionals with domain expertise, such as those from marketing, writing, or operations backgrounds, looking to transition into entry-level AI positions. In these roles, building a portfolio and demonstrating practical skills often matter more than formal education.

Technical roles

Technical roles may require a degree in computer science, statistics, or a related field. Key skills often include programming, data handling, and an understanding of how AI models work.

Many individuals with formal education but limited work experience often ask how to get a job in AI with no experience. The answer typically involves building a portfolio, contributing to open-source work, or completing practical projects. These AI career paths reward beginners who take initiative.

How to get started in AI

Breaking into AI doesn’t mean doing everything at once. Instead, it involves building the right skills and gaining practical experience over time.

Here’s a simple way to get started:

• Pick your lane: Choose between non-technical, semi-technical, or technical roles based on your current strengths and interests.
• Build valuable skills: Focus on one or two skills, such as prompt design, Python, or data analysis, that align with your chosen path. Don’t try to learn everything at once.
• Get credentialed: Short courses or certifications can signal competence. Platforms such as Coursera offer structured learning.
• Build proof of work: Create small, practical projects that demonstrate your ability. This could include sample evaluations, prompt libraries, or simple data projects.
• Work on real AI tasks: Many AI companies hire beginners for flexible remote tasks involving evaluation, testing, training, and research. Look for freelance, contract, or project-based opportunities that allow you to build experience while earning income.

Find entry-level AI work on Mercor

One of the most direct ways to gain real AI experience is to work on live systems. Mercor helps connect experts with AI-training opportunities across various domains, including but not limited to healthcare, finance, law, and writing.

Through a streamlined interview process, experts and generalists can get matched with roles aligned with their skills and experience. Because many of these jobs are flexible and project-based, they can also be a practical way for beginners to gain hands-on experience and build a stronger portfolio over time.

See how rates work in our AI trainer salary guide, then explore AI training opportunities with Mercor to find roles that match your background.

For enterprises building AI systems, Mercor also helps scale access to expert AI trainers and specialists across a range of domains. Learn more about our enterprise AI solutions.

Remember, AI isn’t built in isolation. It’s shaped by people who apply judgment, context, and expertise. Getting started early can let you build valuable experience while contributing to the next generation of AI tools.