Machine Learning Engineer Intern 2026/27

Canva
Sydney, NSWPosted 1 April 2026

Job Description

Join the team changing how the world designs - billions of times over. Hey you. Yes, you. Ready to do the best work of your life? Our internship is a 12-week, remote friendly program running from early December to late February. It’s designed to give you meaningful experience, real ownership, and a summer that sets you up for what’s next. You’ll also have the opportunity to join your team in person at our flagship campus in Sydney to experience the magic of Canva. As an intern, you won’t be on the sidelines. You’ll work on impactful, real-life projects from day one, alongside your team. You’ll be supported by a dedicated Host and Buddy there to help you learn, grow and navigate your Canva Internship journey with confidence. Where and how you’ll work Our flagship campus is in Sydney, Australia. We also have a campus in Melbourne and co-working spaces in Brisbane, Perth and Adelaide. If you call New Zealand home, we have our Auckland co-working space too. Whether you prefer to work from home, join us in person (if you’re nearby), or a mix of both, you’ll have the flexibility to work in a way that supports you and your team. What you’ll be working on As Canva continues to grow, change is part of how we move forward. That means the role you step into today will likely evolve right alongside you. You’ll start by focusing on: Collaborating with your assigned team to design, develop, test and maintain your intern project Developing and maintaining documentation for software development processes, procedures, and guidelines Developing your craft with some of the most talented engineers Interacting with a wide variety of stakeholders from both inside and outside your team to deliver high-quality work in a fast-paced environment. You'll thrive in this role if You are currently enrolled and completing your studies between 2027 and mid-2028. You are based in Australia or New Zealand and have working rights. Please note that Canva will not be able to sponsor visas and relocate for Early Talent roles. You have excellent Computing and Object-Oriented Programming (OOP) Fundamentals. You are comfortable working in Python. You have the ability to conceive and translate statistical or machine learning models to practical, scalable solutions. You have the passion to learn and grow as a technologist. You are excited to explore AI tools and use them in smart, helpful ways to boost your workflow. You are able to commit to the 12-week, full-time summer program from early December 2026 to late February 2027. It would be awesome if you also have: Experience building and deploying machine learning models. Experience with data platforms (e.g. Spark and Hadoop). Experience with machine learning frameworks (e.g. Scikit learn, TensorFlow, PyTorch). Experience with interactive notebooks for exploration of data and initial prototyping. We are welcoming applications for the Machine Learning Engineer Internship until 1st May 2026 , only. Following this advertisement closure date, successful applicants will be reached out to directly to proceed to the next step of our Canva Early Talent hiring process. Here at Canva, we endeavour to respond to every applicant regardless of the outcome, should you not be successful, we will respond to your application within 5 working days after the job closure date. Meet the team Machine Learning Engineers deliver value to Canva’s users by designing, building and maintaining complex systems to apply statistics and machine learning at scale, and multiplying manual effort with data intelligence. This includes making it easy for users to discover over 75M+ templates, photos, videos and elements; messaging, marketing and building engagement with users; and making it easier for users to design. But don’t just take our word for it. Here’s some advice from past Intern, Veronika Sevastyanova , about interning at Canva. “You will make mistakes, and that’s ok. If you never mess up you won’t grow.” Why ... (truncated, view full listing at source)
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