Senior Research Data Engineer

At Canva our mission is to empower the world to design. Were building AI that feels magical and lands real impact for millions of people - helping anyone create with confidence. Were 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

Youll 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. Youll 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 youll 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.

Youre 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 tooling and human-in-the-loop data collection (Label Studio or internal systems).

• Understanding of ML training requirements you know what good data looks like for LLM/VLM fine-tuning and can anticipate downstream issues.

• Experience loading and writing large datasets to/from cloud infrastructure (AWS) and distributed storage systems.

• Strong communication skills: you can work with researchers to scope…