Senior AI Data Engineer: Multimodal Research Pipelines

Canva; Greater London; Hybrid; GBP 90,000 - 140,000; Full time

Job summary

Canva in London is seeking a Machine Learning Engineer to own the data foundations powering our multimodal agent research. You’ll build pipelines, datasets and tooling to turn ambitious ideas into trainable reality, collaborating closely with researchers and product teams.

You’ll design scalable data systems, ensure data quality, and develop annotation workflows with tools like Label Studio, while distributing ML training data across AWS and S3-backed pipelines.

Qualifications

• Production-grade data pipelines experience
• Prompt engineering experience
• Distributed ML training data workflows
• Annotation tooling familiarity (Label Studio)

Responsibilities

• Design and build data pipelines for agent training across text, image, and multimodal sources.
• Build and maintain infrastructure for data loading, storage, and retrieval at scale (S3, distributed systems, streaming pipelines).
• Collaborate with researchers to translate requirements into 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.

Skills

Python programming

ML DevOps

Prompt engineering

Data pipelines

Distributed ML

Tools

Label Studio

AWS

S3

Ray

Job description

Canva in London is seeking a Machine Learning Engineer to own the data foundations powering our multimodal agent research. You’ll build pipelines, datasets and tooling to turn ambitious ideas into trainable reality, collaborating closely with researchers and product teams.

You’ll design scalable data systems, ensure data quality, and develop annotation workflows with tools like Label Studio, while distributing ML training data across AWS and S3-backed pipelines.