Research Engineer - Design Generation Modeling

Join the team redefining how the world experiences design.

Hey, g'day, mabuhay, kia ora,你好, hallo, vítejte!

Thanks for stopping by. We know job hunting can be a little time consuming and you're probably keen to find out what's on offer, so we'll get straight to the point. 

Where and how you can work

Our flagship office is in Sydney, Australia, but we've made our way from down under, to a hub in San Francisco, which is now home to our US operations. We offer flexibility in how and where you work. We trust our Canvanauts to choose the balance that empowers them and their team to achieve their goals.

As a Research Engineer in Design Generation Modeling, you will work in collaboration with researchers across the globe to produce artifacts and knowledge that will help us push the state of the art of generative design modeling forward. The role will require developing and implementing experimental training pipelines from data to training to inference.

At the moment, this role is focused on:

• Foundation models for design understanding and generation

• Visual models for design generation and decomposition

• Development of design representations for discrete modeling with MLMs

Primary Responsibilities:

• Contribute to the development and optimization of ML data pipelines and training workflows.

• Improve internal training codebases and procedures.

• Collaborate with research scientists and ML engineers to design and implement experiments, ablation studies, etc.

You’re probably a match if you:

• Understand and have experience with distributed training at scale using libraries like DeepSpeed, FSDP, Torch, Titan, etc.

• Enjoy diving deep into and understanding complex engineering problems.

• Are familiar with the literature around GANs, diffusion modeling, transformer architectures, and vision language modeling.

• You have disciplined coding practices and are experienced with code reviews and pull requests.

• Have experience working in cloud environments, ideally AWS.

• Are passionate about both product-focused and basic research.

Nice to Haves:

• Specific experience with modeling design data.

• Experience with Graph Neural Networks.

• Are able to quickly prototype ML demos with appealing user interfaces. E.g. Gradio and other customized interfaces.

What's in it for you?

Achieving our crazy big goals motivates us to work hard - and we do - but you'll experience lots of moments of magic, connectivity and fun woven throughout life at Canva, too. We also offer a range of benefits to set you up for every success in and outside of work.

Here's a taste of what's on offer:

• Equity packages - we want our success to be yours too
• Health benefits plans to support you and your wellbeing
• 401(k) retirement plan with company contribution
• Inclusive parental leave policy that supports all parents & carers
• An annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more
• Flexible leave options that empower you to be a force for good, take time to recharge and supports you personally

Check out lifeatcanva.com for more information.

Other stuff to know

We make hiring decisions based on your experience, skills, merit and business needs, in compliance with applicable local laws.
We celebrate all types of skills and backgrounds at Canva so even if you don’t feel like your skills quite match what’s listed above - we still want to hear from you!
When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process. Please note that interviews are conducted virtually.

At Canva, we value fairness, and we strive to provide competitive, market-informed compensation whilst ensuring internal equity within the team in each region. We make hiring decisions based on your skills, experience and our overall assessment of what we observed and learnt in the hiring process. The target base salary range for this position is $180,000 - $225,000. When calculating offers, we make salary decisions based on market data, your experience levels, and internal benchmarks of your peers in the same domain and job level.

Please note that interviews are predominantly conducted virtually.