Remote Senior Machine Learning Engineer

Salary: £70,000 - 70,000 per year

Requirements

• Strong software engineering skills in Python, with experience building production‑grade data pipelines and ML DevOps.
• Practical experience with prompt engineering, designing, testing, and refining prompts for reliable LLM/VLM outputs.
• Experience with ML data workflows, including large‑scale data processing and loading, data versioning, and training format considerations such as tokenization, batching, and 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.
• Understanding of ML training requirements and what good data looks like for LLM/VLM fine‑tuning.
• Experience loading and writing large datasets to and from cloud infrastructure and distributed storage systems.
• Strong communication skills to scope ambiguous problems and translate needs into actionable plans.
• A collaborative approach, with comfort taking ownership and iterating quickly.
• Experience with preference data collection for RLHF or reward modelling.
• Familiarity with multimodal data such as image‑text pairs, video, and design assets.
• Experience building synthetic data generation pipelines using LLMs.
• Background in data quality metrics and monitoring systems.
• Contributions to dataset releases or benchmarks in the ML community.

Responsibilities

• Design and build data pipelines for agent training, including 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.
• 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 by building validation frameworks, monitoring for drift and contamination, and establishing standards for trustworthy and reproducible datasets.
• Document datasets thoroughly, including provenance, known limitations, intended use cases, and versioning history.
• Implement comprehensive test coverage for data pipelines and ML workflows to ensure reliability and catch 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.

Technologies

• AI
• Cloud
• DevOps
• Support
• LLM
• Python
• AWS
• HTTP
• Machine Learning

More

We are Canva, a company redefining how the world experiences design, with our flagship campus in Sydney, Australia and part of our European operations based in Austria. We offer flexibility in where and how you work, trusting our team to choose the balance that helps us achieve our goals. Our Austrian operations are developing AI products within Canva to reimagine how artificial intelligence can be used in design. We also offer equity packages, an inclusive parental leave policy, an annual Vibe & Thrive allowance, and flexible leave options. We celebrate all backgrounds, support virtual interviews, and encourage applicants to share pronouns and any reasonable adjustments needed during the interview process.

last updated 28 week of 2026