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.

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.

More info: https://www.allsides.tech | https://blogs.nvidia.com/blog/covision-adidas-rtx-ai/

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!

This role is located on‑site in Brixen/Bressanone, Italy. If you are interested, please apply with your CV attached to [email protected]

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📌 Dataops & mlops engineer (Albes)
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