Senior Research Data Engineer

About this role

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 research team, developing new multimodal agentic systems. We work on all topics of multimodal modelling, pre/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 at scale. You'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.