Breaking into AI can feel like trying to jump on a moving train — fast-paced, full of opportunity, but hard to grab onto. Whether you're a fresh graduate, self-taught coder, or switching from another field, it's easy to make a few missteps that slow your progress.
The good news? Most early-career mistakes are fixable. Here are 7 common ones to watch out for — and what to do instead.
1. Focusing Too Much on Theory
What it looks like:
Spending months perfecting your understanding of gradient descent, but never building anything beyond a notebook.
Why it’s a problem:
Employers want to see how you apply what you know. If your resume is full of Coursera completions but no practical outputs, it’s hard to judge your real-world skills.
What to do instead:
Build small, focused projects — recommendation systems, image classifiers, chatbots — and share them. It shows initiative, problem-solving, and the ability to finish what you start.
2. Not Showcasing Your Projects
What it looks like:
You’ve done great work in uni or built things in your spare time, but it’s all sitting in folders on your desktop.
Why it’s a problem:
If a recruiter or hiring manager can’t see your work, they can’t assess your potential.
What to do instead:
Put your projects on GitHub. Write short READMEs that explain what the project does, how to run it, and what you learned. Bonus points for live demos, blog posts, or LinkedIn write-ups.
3. Messy (or Private) GitHub
What it looks like:
A GitHub full of broken code, forks of other people’s projects, or private repos with no explanation.
Why it’s a problem:
Your GitHub is often your portfolio. If it’s chaotic or empty, it raises questions about your readiness.
What to do instead:
Curate your GitHub. Pin your best projects. Add documentation. If you're keeping projects private for a reason, consider sharing PDFs or screenshots when applying.
4. Overusing Buzzwords
What it looks like:
Your resume says you “utilised state-of-the-art deep learning frameworks to deliver robust, scalable solutions using cutting-edge AI systems.”
Why it’s a problem:
It sounds impressive… but says almost nothing. Recruiters want clarity, not jargon.
What to do instead:
Use plain, specific language: “Built a CNN in PyTorch to classify X-ray images with 88% accuracy.” Show what you did, how, and the result.
5. Applying Without Tailoring Your Resume
What it looks like:
Sending the same generic resume to 50 jobs — and hearing back from none.
Why it’s a problem:
Hiring managers skim for fit. If your application doesn’t mention the tools or responsibilities listed in the ad, you’ll be overlooked.
What to do instead:
Tweak your resume and cover letter for each role. Highlight matching skills. Use keywords from the job description. Make it easy for them to say yes.
6. Neglecting Communication Skills
What it looks like:
Brilliant at models, but struggling to explain your work in interviews or project summaries.
Why it’s a problem:
In AI teams, communication is everything — whether you're explaining results to stakeholders or writing documentation for others.
What to do instead:
Practice explaining your projects like you’re teaching someone new to the topic. Record yourself or do mock interviews with a friend. Clear communicators stand out.
7. Skipping Junior Jobs Hoping to Land a ‘Researcher’ Role
What it looks like:
Aiming only for elite researcher roles at CSIRO, DeepMind, or Google Brain — without any industry experience.
Why it’s a problem:
These roles are hyper-competitive and often require PhDs or years of experience. You might be missing great stepping stones.
What to do instead:
Look for internships, analyst roles, junior ML engineer or data scientist positions. You’ll build skills, connections, and a track record — fast-tracking your path to senior roles later.
You're Closer Than You Think
Everyone starts somewhere — even the people working in the roles you admire most. What matters isn’t perfection, it’s momentum. By focusing on practical skills, communicating clearly, and showing your work, you're already ahead of the curve.