Artificial intelligence is transforming the job market, but not always in the ways people expect. Despite headlines warning of mass layoffs, recent research reveals a more complex picture: AI is reshaping roles rather than eliminating them.
According to nonprofit, nonpartisan research organization RAND, while some tasks are being automated, more businesses are actually hiring because of AI adoption. Any fears about industry-wide disruptions are unfounded, as McKinsey reports that nearly 90% of companies now use AI in at least one business function, though few have fully scaled it across their organizations.
This is an uncertain time for professionals entering the AI job market, but one full of opportunity if you know where to look. Employers need people who can build, deploy and manage AI systems that are practical, scalable and human-centered. But success in these roles requires more than technical skills alone. Today’s AI professionals must also understand design thinking, ethics, communication and collaboration because the most effective systems are built for people as much as for efficiency.
This post explores what entry-level roles in AI look like today, the skills employers are seeking and how graduate students in applied AI programs can stand out in a fast-changing field.
Career Outlook for Entry-Level AI Jobs
Artificial intelligence continues to expand across industries including healthcare, education, finance, retail, logistics and more, creating a strong demand for professionals who can help organizations adopt and scale AI technologies.
According to CompTIA’s 2025 State of the Tech Workforce, employer job postings mentioning AI skills increased by more than 90% year-over-year. Additionally, the World Economic Forum identifies AI and Machine Learning Specialists as the fastest-growing job title worldwide, with projected job growth of 40% by 2029.
This growth isn’t happening in isolation; AI is evolving alongside cloud infrastructure, data platforms and DevOps practices. As a result, candidates who understand how AI connects with broader tech systems (e.g., cloud services or data pipelines) may have an edge in today’s job market.
As of late 2025, job boards regularly list thousands of AI-related openings. In-demand entry-level roles include data analysts, junior software engineers, machine learning interns and research assistants. However, many roles don’t explicitly include “AI” in the title. If you’re looking for entry-level jobs in artificial intelligence, you need to know how to dig deeper by using broader terms and advanced filters.
7 Common Entry-Level AI Jobs
Below are seven high-visibility entry-level roles for those pursuing a career in applied AI. Each includes a snapshot of responsibilities, requirements, typical employers and salary range.
These roles also align with the most frequently posted early-career positions across the tech workforce, including data analysts, software developers and systems analysts, according to CompTIA’s 2025 report.
| 1. Artificial Intelligence / Machine Learning (ML) Research Assistant | |
This broad entry-level role offers a strong foundation in artificial intelligence by supporting research teams in academic or industry settings. It’s ideal for building hands-on experience with real-world AI/ML projects. | |
| What You’ll Do | Assist with AI/ML research projects by organizing data, training models, and running experiments |
| Employers: | – University research labs – Tech companies – Healthcare systems |
| Requirements | – Bachelor’s or master’s in CS, data science, or related field – Python, ML libraries (e.g., TensorFlow, PyTorch) – Analytical mindset and strong collaboration skills |
| Entry-Level Average Salary Range | $70,000–$117,000 |
| Average Salary Range for All Experience Levels | $81,000–$133,000 |
| 2. Junior / Associate Data Scientist | |
This high-impact role blends statistical analysis and machine learning to generate insights and predictive tools. It’s ideal for early-career professionals who want to build models, experiment with algorithms, and solve practical business challenges. | |
| What You’ll Do | Use machine learning, statistics and modeling to extract insights and build predictive tools |
| Employers: | – SaaS firms – Financial institutions – Retail or healthcare companies |
| Requirements | – Bachelor’s or master’s in data science, computer science (CS) or math – Python, R, SQL; experience with data visualization tools – Familiarity with model evaluation techniques |
| Entry-Level Average Salary Range | $98,000–$151,000 |
| Average Salary Range for All Experience Levels | Use machine learning, statistics, and modeling to extract insights and build predictive tools |
| 3. Junior Data Analyst | |
Often a first step toward data science or AI roles, this position focuses on analyzing datasets, identifying trends and communicating findings to help guide business decisions. It’s a strong entry point for those with sharp quantitative and storytelling skills. | |
| What You’ll Do | Analyze and visualize datasets to support decision-making |
| Employers: | – Government agencies – Marketing and media firms – Logistics and supply chain companies |
| Requirements | – Bachelor’s in stats, business or analytics – Excel, SQL, Tableau or Power BI – Strong communication and reporting skills |
| Entry-Level Average Salary Range | $64,000–$109,000 |
| Average Salary Range for All Experience Levels | $67,000–$120,000 |
| 4. Junior Software Engineer (AI/ML focus) | |
This role combines software development with foundational AI knowledge. It’s designed for coders who want to work on products that integrate machine learning and who are eager to collaborate across technical teams. | |
| What You’ll Do | Contribute to the design and development of ML-powered applications |
| Employers: | – AI startups – Defense and cybersecurity contractors – SaaS product teams |
| Requirements | – Bachelor’s in CS or engineering – Python, Java, Git; experience with REST APIs – Understanding of ML concepts |
| Entry-Level Average Salary Range | $108,000–$173,000 |
| Average Salary Range for All Experience Levels | $137,000–$204,000 |
| 5. Junior Software Developer | |
A versatile entry-level programming role, this position involves writing, debugging and maintaining software that may support AI-driven systems. It’s well-suited to those looking to grow into more specialized development or AI engineering paths. | |
| What You’ll Do | Maintain and enhance code for backend or frontend systems with AI integrations |
| Employers: | – Tech consultancies – Insurance providers – Retail and e-commerce companies |
| Requirements | – Bachelor’s in CS or related field – Proficiency in JavaScript, Python or C++ – Debugging and testing skills |
| Entry-Level Average Salary Range | $89,000–$148,000 |
| Average Salary Range for All Experience Levels | $120,000–$182,000 |
| 6. Information Systems Analyst | |
For those interested in the AI-adjacent IT track, this role involves managing and securing systems that may interface with AI tools. It provides a practical gateway into enterprise tech infrastructure, data workflows and system optimization. | |
| What You’ll Do | Manage, monitor and secure business systems that may interact with AI tools |
| Employers: | – Universities – Hospitals – Large nonprofits |
| Requirements | – Bachelor’s in informational technology (IT), information systems (IS) or CS – Familiarity with system integration, databases and scripting – Problem-solving and communication skills |
| Entry-Level Average Salary Range | $71,000–$110,000 |
| Average Salary Range for All Experience Levels | $95,000–$149,000 |
| 7. Machine Learning Intern / Apprentice | |
Designed for students or recent grads, this hands-on role enables you to gain experience with real ML workflows, from preprocessing data to training models, while learning from experienced practitioners in lab or product environments. | |
| What You’ll Do | Contribute to real-world ML projects through prototyping, testing and data preparation |
| Employers: | – AI labs – Startups – Corporate R&D teams |
| Requirements | – Enrolled in or recent graduate of a CS/AI program – Python, scikit-learn, Jupyter – Willingness to learn and iterate quickly |
| Entry-Level Average Salary Range | $72,000–$131,000 |
| Average Salary Range for All Experience Levels | $101,000–$161,000 |
Note: Salary ranges listed above sourced from Glassdoor as of November 2025. Actual compensation may vary based on geographic location, employer size, years of experience, industry sector and other factors.
How to Gain Experience for Entry-Level Positions
Breaking into AI doesn’t require a decade of experience, but it does require strategy, consistency and tangible proof of your skills. Here’s a roadmap to help you build confidence, credibility and real-world capabilities over your first year of preparation.
0–3 Months: Build Your Foundation
- Learn Python and foundational data tools (NumPy, pandas, SQL)
- Complete 2–3 micro-projects (e.g., data cleaning, visualizations, simple regressions)
- Set up your GitHub and start uploading code
- Explore basic machine learning concepts
- Experiment with prompt engineering and large language model (LLM) APIs (such as OpenAI or Claude)
- Get familiar with cloud platforms (AWS, Azure) and how AI models are deployed at scale; these are increasingly mentioned in job descriptions for AI-adjacent roles
3–6 Months: Level Up Your Portfolio
- Create one complete portfolio project with:
- A working data pipeline
- A basic ML model
- A simple front-end or API for interaction
- Contribute to open-source AI or data projects on GitHub
- Start applying to internships or volunteer tech roles
6–12 Months: Specialize and Apply
- Complete 1–2 end-to-end projects with documentation and UX in mind
- Choose a specialty area (e.g., Natural language processing (NLP), computer vision, AI product management)
- Apply to apprenticeships and junior roles that align with your focus
- Consider formal education such as an MS in Applied Artificial Intelligence to advance faster and access mid-career roles sooner
Portfolio Checklist: What Employers Look For
In entry-level AI hiring, your portfolio often speaks louder than your résumé. Employers want to see how you apply what you’ve learned. Your projects should show your ability to solve problems, write clean code and explain your thinking clearly. Aim for a well-rounded mix of smaller skill demos and more robust, end-to-end builds.
Make sure your portfolio includes:
- At least 2 small projects that showcase specific skills (e.g., exploratory data analysis, linear regression, clustering)
- 1 or more full projects that follow an end-to-end pipeline (data → model → application)
- Clear, well-commented code and detailed README files for each project
- Visualizations or dashboards that explain the outcome or value of your work
- A link to your GitHub profile with regular commits showing consistent progress
- Bonus: A short demo video or blog post explaining your thought process
Entry-Level AI Certificates to Explore
Certificates aren’t a replacement for experience, but they can be a smart way to fill knowledge gaps and signal your commitment to learning. Especially if you’re pivoting into AI from another industry or academic background, these programs help validate your skills and often come with hands-on projects you can include in your portfolio.
Here are several strong starter options:
| Certificate Program | What It Covers | Duration |
| Google Data Analytics Professional Certificate (Coursera) | Foundational data analytics, visualization and SQL skills | 6 months (self-paced); free audit available |
| IBM Machine Learning with Python (Coursera) | Intro to machine learning, algorithms, Python-based modeling | 3 months (self-paced); free audit available |
| Microsoft Azure AI Fundamentals | AI workloads, ML models, responsible AI within Azure | 1 day; $99 for exam |
| AWS Certified AI Practitioner (Amazon) | AI, ML and generative AI concepts in cloud environments | 1–2 hours prep; $100 for exam |
| Deeplearning.AI’s Prompt Engineering for ChatGPT | Prompt engineering and LLM applications (no formal certificate) | 1.5 hours; free |
Where to Find Entry-Level AI Jobs
Whether you’re browsing large job platforms or more specialized sites, there are many resources that can help you discover entry-level roles in artificial intelligence. Below are several worth exploring, each with tools or filters to support early-career applicants.
| Platform | Description | Tips for Entry-Level AI Job Seekers |
| One of the most comprehensive job platforms with robust networking tools | Use keyword filters, such as “entry level” + “AI” or “machine learning,” and set job alerts | |
| Indeed | Aggregates listings from a wide range of employers across industries and locations | Use advanced filters to narrow by experience level, salary and location |
| Built In | Curated job board dedicated to artificial intelligence careers | Browse by city and check the “entry level” tag under experience filters |
| AIJobs.ai | Summarize key capabilities here. | Use the “Intern” filter and/or “Entry-level” tag for early-career positions |
| The Rundown AI Jobs | Newsletter-based job aggregator with weekly postings in AI/ML | Subscribe for updates and explore the jobs tab for curated roles |
| TrueUp.io | Focuses on hiring data for tech and AI roles, with a strong emphasis on transparency | Check out the “AI” category and sort by entry-level or internship tags |
| HiringCafe | Fast-growing job search engine with deep filter options and startup visibility | Use the “Experience” filter and explore roles by company size, funding stage or benefits |
| USAJobs – AI Portal | U.S. government’s AI-specific job portal, listing internships and fellowships | Filter by “Student” or “Recent Graduate” for early-career public sector roles. |
You can also monitor the careers pages of organizations known to hire for AI roles, such as OpenAI, NVIDIA, Anthropic or Hugging Face, for new entry-level opportunities.
If you’re interested in starting your artificial intelligence career, consider the University of San Diego’s Master of Science in Applied Artificial Intelligence. This innovative online program is an ideal way to obtain the experience and knowledge needed for many entry-level positions. If you’re interested in learning more, please reach out to one of our enrollment advisors.
