If you’re a student, recent grad, or career changer looking to break into the world of Artificial Intelligence (AI), you’re not alone—and you’re not too late. AI is one of Australia’s fastest-growing tech fields, and there’s never been a better time to explore entry-level AI jobs, junior machine learning roles, and graduate AI programs.
Whether you're studying computer science, teaching yourself Python after hours, or pivoting from another career entirely, this guide will walk you through how to get started, what skills to build, and how to land that first role.
What Are Entry-Level AI Roles?
When people hear “AI,” they often think of advanced robotics or science fiction-level tech. But in the job market, entry-level AI roles are much more grounded—and accessible.
Here are some common job titles you might see:
- Junior Machine Learning Engineer
- AI/ML Intern
- Data Science Graduate
- NLP Assistant Researcher
- Computer Vision Intern
- AI Software Developer
These roles often focus on supporting senior engineers or researchers. You might help clean datasets, train models, run experiments, or write code that integrates AI features into real products.
Top Skills to Learn
You don’t need a PhD to get started—but you will need some solid foundations. Focus on the following:
Programming & Tools
- Python (most popular language in AI)
- Jupyter Notebooks, GitHub
- TensorFlow, PyTorch (pick one to start)
Data Skills
- Pandas, NumPy for data wrangling
- SQL for data access
- Basic data visualisation with Matplotlib or Seaborn
ML & AI Concepts
- Supervised vs. unsupervised learning
- Neural networks (just the basics to start)
- Common models: decision trees, logistic regression, CNNs
Bonus Skills
- APIs (how to send/receive data from models)
- Web basics (HTML/CSS/JavaScript) for AI product work
- Knowledge of AI ethics and bias
Best Pathways: Courses, Internships, and Certifications
There’s no single “correct” way into AI. Choose a mix of learning and hands-on experience that suits your goals.
Study Options
- University degrees in Computer Science, Data Science or AI (many now include industry projects)
- Short courses like:
Internships & Graduate Programs
Look for:
- AI internships through university career hubs or job boards.
- Graduate programs:
DIY Projects
- Build your own ML models using Kaggle datasets
- Try small AI apps (e.g., sentiment analysis on tweets)
- Contribute to open-source ML repos on GitHub
Tips for Landing Your First Role
It’s competitive—but manageable. Here’s how to stand out:
1. Build a Portfolio
- Use GitHub to host your code
- Include a README that explains what the project does
- Add visuals or results (charts, sample outputs)
2. Tailor Your Resume
- Focus on projects, even if they’re self-taught
- Use keywords from the job ad (e.g., “TensorFlow,” “data preprocessing”)
3. Network Strategically
- Join AI meetups in your city (e.g., Sydney ML Meetup)
- Follow AI professionals on LinkedIn
- Ask for coffee chats or mentorship (most people are keen to help!)
4. Apply Broadly—but Smartly
- Don’t wait for the “perfect” job ad
- Look for adjacent roles (e.g., data analyst with AI tools)
- Apply even if you meet most but not all the criteria
You’ve Got This
AI is a booming field—and it's not just for people with PhDs. Whether you're coming from uni, bootcamp, or another career entirely, there’s a place for you.
It’s normal to feel a bit overwhelmed, but if you stay curious, keep learning, and take action, you’ll be surprised how quickly you can break into the space.