ML/Data Infrastructure Engineer

About the role

As a member of the Autonomy team, you will design, build and operate the data and machine‑learning infrastructure that powers our inspection drones. You will own end‑to‑end workflows from raw data collection to model deployment, enabling faster AI development for hazardous‑environment robotics.

Key responsibilities

• Own the infrastructure and pipelines for ML data pools, including ingestion, curation, validation and versioning.
• Host and manage in‑house labeling tools and annotation workflows, coordinating with external labeling partners.
• Automate training, evaluation, experiment tracking and reproducibility pipelines.
• Build tooling to move models from training to reliable production deployment.
• Monitor model performance in production and feed failure cases back into the development loop.

Required profile

• 3+ years of experience in data engineering, MLOps or related software engineering.
• Strong Python programming skills and experience building production‑grade systems.
• Hands‑on experience with AWS services (especially S3) and cloud‑based compute/storage.

Required skills

• Python
• AWS (S3, compute)
• Docker
• CI/CD pipelines
• Workflow orchestration / infrastructure‑as‑code
• Familiarity with MLflow, DVC or SageMaker (nice to have)

What we offer

• 25 vacation days plus public holidays, with additional seniority‑based days.
• Flexible schedule and up to two remote days per week.
• Comprehensive accident insurance.
• Gym discounts, Swibeco rewards and regular team events.