Staff Machine Learning Engineer – Recommendations

Canva

Staff Machine Learning Engineer in the Recommendations team at Canva, delivering personalized recommendations using advanced ML techniques. Lead design, implementation, and improvement of machine learning models.

Posted 6/30/2026full-timeRemote • 🇦🇺 AustraliaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying production-scale recommendation systems using Python and core machine learning tools. Strong analytical and communication skills facilitate effective collaboration with cross-functional teams and mentorship of machine learning engineers.

Highest-signal resume keywords

Production-Scale Recommendation SystemsPython ProgrammingMachine Learning Tooling (PyTorch, Pandas, Scikit-Learn, Numpy)Model Performance EvaluationCross-Functional Team Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning Model DevelopmentModel EvaluationTraining PipelinesModel ServingData-Driven Decision Making

Soft Skills

Excellent CommunicationCollaborative MindsetMentorship

Tools & Technologies

PyTorchPandasScikit-LearnNumpy

Industry Keywords

Machine LearningRecommendation SystemsData-Driven CallsTechnical Documentation

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

You'll design, implement, and refine machine learning models to deliver personalized recommendations, taking on the most complex and ambiguous parts of the system.
You'll improve the architecture, code structure, and performance of our machine learning systems, helping raise the engineering quality bar across the team.
You'll investigate research papers and state-of-the-art machine learning models, and judge which ones are worth bringing into production at Canva.
You'll lead the design of online and offline experiments to validate model performance, and make data-driven calls on what to ship and what to iterate on.
You'll shape how our machine learning models integrate with the broader technology stack, ensuring strong performance and reliability across the pipeline.
You'll work closely with product and engineering teams to deploy new recommendation features, driving alignment when initiatives span multiple teams.
You'll document and communicate your work to both technical and non-technical stakeholders — including senior leadership — and mentor MLEs around you through reviews, pairing, and shared knowledge.

Requirements

What you’ll need

Significant experience developing, shipping, and operating production-scale recommendation systems.
Proficiency in Python and core ML tooling (PyTorch, pandas, scikit-learn, numpy), with strong fluency on the engineering side of ML — training pipelines, evaluation, and serving.
Strong analytical skills, with a track record of rigorously evaluating model performance and making data-driven calls on what to ship and what to iterate on.
Excellent communication skills, with the ability to explain complex technical concepts to a wide range of audiences — including senior leadership.
A collaborative mindset and a passion for working with cross-functional teams to achieve shared goals, including supporting the growth of MLEs around you.

Benefits

Comp & perks

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