Canva
Senior Machine Learning Engineer- Multimodal Data
Vaga remota de Machine Learning Engineer com fit claro de localização do candidato.
Publicada4 de jul. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Áustria
Resumo da vaga
Senior Machine Learning Engineer- Multimodal Data
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
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 DevOps.
- 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 ambiguous problems and translate needs into actionable plans.
- A collaborative approach, comfortable taking ownership and iterating quickly.
Nice to haves
- Experience with preference data collection for RLHF or reward modelling.
- Familiarity with multimodal data (image-text pairs, video, 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.
Here's a taste of what's on offer:
- Equity packages - we want our success to be yours too
- Inclusive parental leave policy that supports all parents & carers
- An annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more
- Flexible leave options that empower you to be a force for good, take time to recharge and supports you personally
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