{"id":29736,"url":"https://alion.io/job/canva-senior-machine-learning-engineer-research-optimisation","title":"Senior Machine Learning Engineer - Research Optimisation","company":{"id":181,"name":"Canva","domain":"canva.com","url":"https://alion.io/company/canva","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":13,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":33,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sydney, Australia"],"countries":["AU"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":128000,"max_usd":330000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2102},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AWS","optional":false},{"name":"Canva","optional":false},{"name":"CI/CD","optional":false},{"name":"DeepSpeed","optional":false},{"name":"FSDP","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"LLM","optional":true},{"name":"Megatron-LM","optional":true},{"name":"Multimodal AI","optional":true},{"name":"ONNX","optional":true},{"name":"TensorRT","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-06-17T15:58:18Z","employer_posted_date":"2026-06-17","last_verified_at":"2026-10-18T04:13:00Z","board_verified":true,"closed_at":null,"days_open":122,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":122},"description":"Join the team redefining how the world experiences design.\nHey, g'day, kia ora, 你好, hallo, vítejte!\nThanks 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.\nWhere and how you can work\nOur flagship Sydney campus is uniquely Canva - an extension of our Surry Hills neighbourhood. It’s a thoughtfully designed space with plenty of room to collaborate, focus, and connect.\nThis role is based in Sydney, and we’re looking for someone who calls it home. Our hybrid way of working gives you the flexibility to work remotely, and to come together on campus for meaningful in-person collaboration and connection when it matters most. We trust our Canvanauts to choose the balance that empowers them and their team to achieve their goals.\n 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 performance, turning promising experiments into stable, scalable, user-facing capabilities while making training and inference faster, cheaper, and more reliable.\nAbout the role:\nYou'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. You'll also work across the training stack, profiling and tuning PyTorch workloads, improving GPU utilisation, and shaping how we use distributed training and storage to get the most out of our compute. 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.\nAt the moment, this role is focused on:\nResearch-to-Production Pipeline: Hardening experimental models (containerisation, tests, CI/CD), making them deployable for real users.\n\nTraining Performance and GPU Efficiency: Profiling PyTorch training jobs, improving GPU utilisation, and applying techniques like mixed precision, efficient data loading, and distributed training strategies (FSDP, DDP, DeepSpeed) to reduce time and cost per experiment.\n\nLibrary development: Converting experiments into well-factored libraries with clear APIs, dependency hygiene, and versioning, so teams can import rather than copy-paste.\n\nDeveloper Experience & Documentation: Creating templates, examples, and guidance; offering supportive, high-signal communication so others can adopt libraries confidently.\n\nReliability, Observability & Cost: Instrumenting services with metrics/logging/tracing, setting SLIs/SLOs, and optimising training and inference performance and spend.\n\nPrimary Responsibilities:\nProductionise research models: refactor, test, containerise, and integrate them into the monorepo for scalable reuse.\n\nProfile and optimise PyTorch training jobs, working with researchers to identify bottlenecks across compute, memory, I/O, and networking.\n\nImprove distributed training setups (multi-GPU, multi-node) and help teams pick the right parallelism strategy for their workload.\n\nBuild and maintain inference services, SDKs, and shared libraries that standardise pre/post-processing and interfaces across variants.\n\nCreate multi-variant runners and rollout frameworks (feature flags, canaries, A/B testing, automated rollbacks).\n\nEstablish CI/CD workflows, artifact management, and reproducible builds for ML services and model assets.\n\nAdd robust observability (dashboards, alerts) and reliability practices (load tests, chaos/resiliency checks) across training and inference workloads.\n\nOptimise inference (batching, caching, quantisation/compilation, hardware utilisation) to reduce latency and cost.\n\nWork across the broader training stack, including Kubernetes orchestration, storage (e.g. Weka, Vast, Lustre), and data pipelines, to remove friction for research teams.\n\nPartner with researchers and product engineers via code reviews, pair sessions, and clear documentation to accelerate adoption.\n\nDrive good engineering hygiene in the research codebase: testing strategy, dependency management, and de-duplication across multiple model variants.\n\nYou're probably a match if you:\nHave strong software engineering fundamentals and excellent Python skills; you're comfortable turning notebooks and prototypes into production-grade services.\n\nHave shipped ML systems in production (containers, APIs, CI/CD), ideally within a monorepo environment.\n\nHave hands-on experience optimising PyTorch training or inference, profiling workloads, and reasoning about GPU memory, compute, and throughput.\n\nAre comfortable in containerised environments and understand Kubernetes concepts well enough to debug and improve ML workloads running on it.\n\nCan read research code and refactor it into clean abstractions with stable, well-documented interfaces.\n\nUnderstand service reliability and observability (metrics, tracing, logging) and how they apply to ML systems.\n\nThink holistically about the stack, from storage and networking through to model code, and can hold a credible conversation with researchers, DevOps, and platform engineers alike.\n\nCommunicate clearly and empathetically, especially when guiding others to adopt libraries and best practices and mentoring engineers earlier in their ML journey.\n\nBring cloud experience (AWS a plus) without needing to be a deep specialist.\n\nNice to Have:\nFamiliarity with model-serving/optimisation tooling (e.g., ONNX, TorchScript, Triton, quantisation).\n\nExperience writing or optimising CUDA kernels, or using compilation frameworks (torch.compile, Triton, TensorRT) to speed up models.\n\nExperience with distributed training frameworks (FSDP, DDP, DeepSpeed, Megatron) at meaningful scale.\n\nFamiliarity with high-performance storage systems (Weka, Vast, Lustre) and the data loading patterns that make or break training throughput.\n\nExperience with experimentation platforms (feature flags, A/B testing) and safe rollout strategies.\n\nBackground with multimodal/image generation stacks or LLM-adjacent tooling (not the core focus, but helpful).\n\nKnowledge of MLOps practices (model registries, artifact stores, dependency/version management).\n\nImpact you'll have:\nYou'll dramatically reduce the time it takes to move from a successful experiment to a reliable, observable feature in production. You'll eliminate copy-paste, unify interfaces, enable parallel variants, and build the shared foundations that let Canva ship ML innovation at scale. You'll also help our research teams get more out of every GPU hour, making training faster and inference cheaper as we scale up the work CORE is doing.\n What's in it for you?\nAchieving 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.\nHere's a taste of what's on offer:\nEquity packages - we want our success to be yours too\nInclusive parental leave policy that supports all parents & carers\nAn annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more\nFlexible leave options that empower you to be a force for good, take time to recharge and supports you personally\nCheck out lifeatcanva.com for more info.\nOther stuff to know\nWe make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture. We see AI as a powerful amplifier of creativity and technology at Canva. We’re evolving how we assess AI skills in our Technology hiring experience - you’ll tackle interactive, real-time challenges that reflect the kind of work we do. 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