• Lead the Fraud & Risk Machine Learning organization, managing a team responsible for production fraud detection models. • Define and execute the machine learning roadmap for fraud prevention, identity verification, and risk decisioning. • Build and scale a portfolio of production ML models from concept through deployment and continuous optimization. • Partner with Product, Engineering, Risk, and Executive Leadership to solve complex business challenges using machine learning. • Drive end-to-end machine learning development including: Feature engineering, Data preparation, Model development, Validation, Production deployment, Monitoring and model performance optimization. • Establish best practices for model governance, experimentation, and production reliability. • Mentor and grow a high-performing team of Data Scientists and Machine Learning Engineers. • Provide technical leadership while remaining capable of contributing hands-on when necessary. • Present technical strategy, business impact, and model performance to executive stakeholders.

Role Description Provision and manage virtual machines across multiple environments. Perform OS configuration, patching, and basic hardening across Linux and Windows systems. - Monitor VM health including CPU utilization, memory usage, disk performance, and service availability. - Assist with backup configuration and validation procedures. - Support restore testing activities for virtual systems. - Troubleshoot VM performance issues and boot related incidents. - Maintain VM inventory and infrastructure documentation. - Assist with operational monitoring and alert investigation. - Support environments that follow federal security and compliance requirements. - Collaborate with cloud engineers and infrastructure teams during system changes. Qualifications - Need CompTIA Security+ certification Requirements - DoD 8570 / 8140 Security+ or equivalent - 7-10 years of experience supporting enterprise infrastructure systems or virtual platforms - Bachelor's Degree

• Design and implement deep learning architectures for 3D volumetric medical imaging (CT, PET, MRI) • Develop survival models that handle censored outcomes, competing risks, and the statistical nuances of time-to-event prediction • Optimize training pipelines to efficiently process large-scale imaging datasets on cloud GPU infrastructure • Collaborate with our ML team to establish best practices and push the state of the art • Contribute to research publications and present findings at conferences

Role Description As a Senior Machine Learning Engineer on Elements Platform, you'll build the ML systems that power how style gets understood — and how that understanding gets reflected in the elements generated for people's designs. Beyond that, you'll help shape how templates and elements get generated at scale. You'll work closely with the Content Engine, Core Research, and AI Media teams to take projects from a strong idea to a dependable, scaled system. This is a role for someone who wants to own real ML infrastructure — not just train models, but ship, monitor, and improve systems that directly shape what millions of people see when they open Canva. You'll have real autonomy to identify problems, propose solutions, and see them through to production. Responsibilities - Design, build, and ship ML models that power content generation and quality eval scoring for Canva's generated element and template library. - Own the full ML lifecycle — from data pipelines and training through to deployment, monitoring, and iteration. - Partner with Content Engine, CORE AI Research, AI Media, and Discovery teams to align ML work with the broader content strategy. - Diagnose and resolve issues across model performance, data quality, and production reliability. - Run experiments to validate ML approaches before scaling them, with clear success metrics. - Contribute to the engineering standards and tooling that make ML development faster and more reliable across the group. - Communicate technical tradeoffs clearly to both engineering and non-engineering stakeholders. Qualifications - Solid experience building and deploying machine learning systems in production, not just prototyping them. - Comfortable owning a problem end-to-end, from framing it, to building the model, to making sure it holds up once it's live. - Care about the practical tradeoffs between model quality, latency, and cost. - Ability to make good calls under uncertainty. - Collaborate well with engineers, product and content teams. - Motivated by seeing your work show up in something millions of people use every day. - Curiosity and a willingness to dig into ambiguous problems. What You'll Learn & Build - Deep experience scaling ML systems for a content platform used by hundreds of millions of people. - Exposure to the full spectrum of content ML work — from generation to design understanding. - Opportunities to shape ML engineering practice across the Content Product group. - Growth toward technical leadership, with real scope to define and drive your own roadmap. Benefits - Equity packages - we want our success to be yours too. - 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. Additional Information We make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture. When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process. All interviews are conducted virtually.