ML Data Infrastructure Engineer

You will build and operate infrastructure for ML datasets, labeling, training, evaluation, deployment, monitoring, and developer workflows. You will curate and version datasets, host labeling tools, automate model lifecycle processes, and provide reliable tools and documentation for ML engineers.

Responsibilities

• Own infrastructure and workflows for ML data pools and datasets
• Host labeling tools and manage annotation workflows
• Automate training, evaluation, experiment tracking, and reproducibility workflows
• Build tooling for model validation and deployment
• Monitor model performance and connect failure cases to ML development
• Provide ML developer tools, documentation, and workflows

Requirements

• 3+ years of data engineering, MLOps, ML infrastructure, or related software engineering experience
• Python and software engineering skills
• Experience with data storage, databases, and data processing
• AWS experience, particularly S3 and cloud compute and storage
• Experience with the ML lifecycle
• Experience with ML dataset curation, versioning, annotation, and data quality
• Experience with Docker, CI/CD, workflow orchestration, or infrastructure-as-code
• English proficiency

Benefits

• 25 vacation days per year plus public holidays
• Up to 5 additional seniority-based days off
• Accident insurance covering medical treatment and hospitalization
• Flexible schedules
• Remote work up to 2 days per week
• Discounts on gym memberships and sports events
• Swibeco discounts and rewards
• Team events