Remote Senior Machine Learning Engineer - Multimodal Data

Join the team redefining how the world experiences design.

Servus, 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 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.

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!

At Canva, our mission is to empower the world to design. We’re building AI that feels magical and lands real impact for millions of people - helping anyone create with confidence. We're looking for a Machine Learning Engineer to own the data foundations that power our multimodal agent research—building the pipelines, datasets, and tooling that turn ambitious research ideas into trainable reality.

About the team

We explore multimodal agentic architectures, build scalable training and evaluation loops, and partner closely with product and platform teams to turn breakthroughs into delightful product features. We are a cutting-edge post-training team, developing new multimodal agentic systems. We work on all topics of multimodal modelling, post-training and design agents, we build scalable training and evaluation loops, and partner closely with product and platform teams to turn breakthroughs into delightful product features.

About the role

You'll be responsible for the data lifecycle that fuels our agent research: from collection and curation through to preprocessing, quality assurance, and delivery into training pipelines. You'll work closely with research scientists to understand what data is needed, then design and build the systems to make it happen—reliably and 'll have significant autonomy over how data problems get solved, while aligning on what problems matter most with the broader team.

What you'll do

• Design and build data pipelines for agent training: collection, filtering, deduplication, formatting, and versioning across text, image, and multimodal sources.

• Build and maintain infrastructure for efficient data loading, storage, and retrieval at scale (S3, distributed systems, streaming pipelines).

• Collaborate with research scientists to translate research requirements into concrete data specifications, and iterate as experiments reveal new needs.

• Create evaluation datasets and benchmarks in collaboration with researchers—curating task distributions that surface real failure modes.

• Develop tooling for dataset construction—including human annotation workflows, synthetic data generation, and preference data collection for RLHF/DPO-style training.

• Own data quality: build validation frameworks, monitor for drift and contamination, and establish standards that make datasets trustworthy and reproducible.

• Document datasets thoroughly: provenance, known limitations, intended use cases, and versioning history.

• Implement comprehensive test coverage for data pipelines and ML workflows, ensuring reliability and catching regressions early.

• Elevate codebase quality through code reviews, refactoring, and establishing engineering best practices that help research velocity scale sustainably.

• Contribute to team roadmaps by identifying data bottlenecks and proposing solutions that unblock research velocity.

You're likely a match if you have

• Strong software engineering skills in Python, with experience building production-grade data pipelines and ML Dev Ops.

• Practical experience with prompt engineering—designing, testing, and refining prompts for reliable LLM/VLM outputs.

• Experience with ML data workflows: large-scale data processing and loading (Ray, or similar), data versioning, and format considerations for training (tokenization, batching, sharding).

• Hands-on experience working with data pipelines for large-scale distributed ML training runs.

• Familiarity with annotation…