About ALLSIDES
ALLSIDES is redefining how the world experiences 3D content. We combine physically accurate scanning and generative AI to power content creation workflows for e-commerce, virtual environments, and immersive experiences. Our clients include global brands like adidas, Meta, Amazon, and Zalando. È pronto/a a candidarsi? Si assicuri di aver compreso tutte le responsabilità e i compiti associati a questo ruolo prima di procedere. We operate a rapidly scaling photorealistic 3D scanning operation, capturing tens of thousands of assets annually while training next-generation AI models. As an NVIDIA Inception member, we collaborate with leading research institutions and actively participate in top-tier conferences in 3D computer vision and AI.
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Position Overview
We're looking for a DataOps & MLOps Engineer to build the infrastructure that powers our data and ML workflows. You'll focus on data storage and movement, dataset versioning, ML pipeline automation, experiment tracking, and ensuring reproducibility across our 3D reconstruction and training workloads.
Main Responsibilities
Design and manage data storage systems for large datasets (multi-TB image data, 3D assets, training data) Build efficient data access patterns and movement strategies for distributed training and experimentation
Implement dataset versioning and lineage tracking for reproducibility
Set up and maintain experiment tracking and model registry infrastructure (MLflow, Weights & Biases) Build ML pipelines for data preprocessing, training, validation, and model registration (Kubeflow, Airflow, Prefect) Support distributed training workflows across multi-GPU clusters (PyTorch Distributed, Horovod, Ray) Profile and optimize training pipelines: data loading bottlenecks, batch sizing, GPU memory utilization
Ensure reproducibility of experiments: environment pinning, data versioning, artifact management
Manage artifact storage and distribution (Docker registries, model registries, package repositories) Build tooling to improve developer productivity for ML workflows
Qualifications
Strong Linux knowledge
Experience with data storage systems and large file handling (object storage, NFS, distributed filesystems) Knowledge of dataset versioning tools (DVC, Delta Lake, or similar) Experience with ML pipeline orchestration (Airflow, Prefect, Kubeflow) Familiarity with experiment tracking tools (MLflow, Weights & Biases, Neptune) Understanding of distributed training frameworks and patterns
Experience with containerization (Docker) and CI/CD pipelines
Knowledge of Python dependency and environment management
Nice to Have
Experience with model registries and deployment workflows
Familiarity with data quality validation frameworks
Knowledge of 3D graphics processing or computer vision workflows
What we offer
Compensation that reflects your experience including stock-options
Lunch voucher for working days
We assist with relocation
Flexible working hours and work-from-home policy
Family-friendly environment
Amazing office space in South Tyrol, located at the Durst Group
Personal and professional growth opportunities
You don't have to tick every box to apply, your drive and passion matter most! xdwybme
This role is located on-site in Brixen/Bressanone, Italy. If you are interested, please apply with your CV attached to