Senior Machine Learning Engineer - Research Enablement

<h3>Company Description</h3><p><strong>Join the team redefining how the world experiences design.</strong></p><p>Servus, hey, g'day, mabuhay, kia ora, 你好, hallo, vítejte!</p><p>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.</p><p><strong>Where and how you can work</strong></p><p>Our flagship campus is in Sydney, Australia but Austria is home to part of our European operations. And you have choice in where and how you work, we trust our Canvanauts to choose the balance that empowers them and their team to achieve their goals.</p><p>Fun fact, a big part of our Austrian operations is developing the AI product within Canva to help reimagine how artificial intelligence can be used in design. Pretty cool ha!</p><h3>Job Description</h3><p>At Canva, our mission is to empower the world to design. To get cutting-edge research into the hands of millions of users faster, we’re looking for a Machine Learning Engineer focused on research enablement and productionisation, turning promising experiments into stable, scalable, user-facing capabilities.</p><p><strong>About the role:</strong></p><p>You’ll be the bridge between research and production. Partnering closely with researchers, you’ll ensure experimental code is production ready, integrate models into our monorepo, build shared libraries and services, and create the tooling and processes that let multiple model variants ship safely and quickly. Your work shortens the research-to-user loop, reduces duplication, and ensures our ML features are reliable, observable, and easy for other teams to adopt.</p><p>At the moment, this role is focused on:</p><ul><li><p>Research-to-Production Pipeline: Hardening experimental models (containerisation, tests, CI/CD), making them deployable for real users.</p></li><li><p>Library development: Converting experiments into well-factored libraries with clear APIs, dependency hygiene, and versioning—so teams can import rather than copy-paste.</p></li><li><p>Multi-Variant &amp; Parallel Execution: Enabling multiple model variants to run in parallel (for canaries, A/Bs, and rollbacks) across our image-generation and related codebases.</p></li><li><p>Developer Experience &amp; Documentation: Creating templates, examples, and guidance; offering supportive, high-signal communication so others can adopt libraries confidently.</p></li><li><p>Reliability, Observability &amp; Cost: Instrumenting services with metrics/logging/tracing, setting SLIs/SLOs, and optimising inference performance and spend.</p></li></ul><p><strong>Primary Responsibilities:</strong></p><ul><li><p>Productionise research models: refactor, test, containerise, and integrate them into the monorepo for scalable reuse.</p></li><li><p>Build and maintain inference services, SDKs, and shared libraries that standardise pre/post-processing and interfaces across variants.</p></li><li><p>Create multi-variant runners and rollout frameworks (feature flags, canaries, A/B testing, automated rollbacks).</p></li><li><p>Establish CI/CD workflows, artifact management, and reproducible builds for ML services and model assets.</p></li><li><p>Add robust observability (dashboards, alerts) and reliability practices (load tests, chaos/resiliency checks).</p></li><li><p>Optimise inference (batching, caching, quantisation/compilation, hardware utilisation) to reduce latency and cost.</p></li><li><p>Partner with researchers and product engineers via code reviews, pair sessions, and clear documentation to accelerate adoption.</p></li><li><p>Drive good engineering hygiene in the research codebase: testing strategy, dependency management, and de-duplication across multiple model variants.</p></li></ul><p><strong>You’re probably a match if you:</strong></p><ul><li><p>Have strong software engineering fundamentals and excellent Python skills; you’re comfortable turning notebooks and prototypes into production-grade services.</p></li><li><p>Have shipped ML systems in production (containers, APIs, CI/CD), ideally within a monorepo environment.</p></li><li><p>Can read research code and refactor it into clean abstractions with stable, well-documented interfaces.</p></li><li><p>Understand service reliability and observability (metrics, tracing, logging) and how they apply to ML systems.</p></li><li><p>Communicate clearly and empathetically—especially when guiding others to adopt libraries and best practices.</p></li><li><p>Bring cloud experience (AWS a plus) without needing to be a deep specialist.</p></li></ul><p><strong>Nice to Have:</strong></p><ul><li><p>Java experience (or JVM ecosystem) for services that integrate with ML components.</p></li><li><p>Familiarity with model-serving/optimisation tooling (e.g., ONNX, TorchScript, Triton, quantisation).</p></li><li><p>Experience with experimentation platforms (feature flags, A/B testing) and safe rollout strategies.</p></li><li><p>Background with mu