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# AI Engineer

- **Company:** Postaladdress
- **Location:** München, Germany
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Cloud Computing, Linux, Revision Control Systems, Job Scheduling, Python (Programming Language), Pytorch, Deep Learning, ONNX (Open Neural Network Exchange) Format, Slurm, Machine Learning Operations, Docker
- **Published:** September 9, 2026
- **Apply:** https://startup.jobs/ai-engineer-model-training-and-deployment-clera-9959428

## About the Role

* At least 1 year of hands-on experience training deep learning models using Python and PyTorch, whether from academic projects, a thesis, or industry work.
 * Solid Python skills with practical PyTorch experience for model development.
 * Experience with Docker for environment replication and model deployment.
 * Comfort working in a Linux or command-line environment.
 * Familiarity with cluster job scheduling tools such as Slurm, or similar systems.
 * Experience with ONNX or other model export and interoperability tools.
 * Background or strong interest in computer vision, robotics, or physical systems deployment.
 * Interest in edge computing and on-device model deployment.
 * Cloud deployment experience is a plus.
 * Familiarity with data versioning tools such as DVC is a plus.
 * Fluency in English; German is a bonus., This role is fully on-site in Munich, Bavaria, Germany. Candidates must be willing and able to work from the Munich office.

## Description

This is an entry-level AI Engineer position on a small, hands-on ML and robotics team building deep learning models that power intelligent robotic work cells in real industrial environments. You will contribute directly to the core product, with early ownership over model training pipelines and deployment to customer sites. It is an opportunity to grow into MLOps and edge deployment as the stack matures., * Train and implement deep learning models as a core part of the robotics product.

 * Iterate on model architectures and training pipelines to improve real-world performance.
 * Support deployment of trained models to customer and on-site edge environments.
 * Collaborate with the ML and robotics team on the technical roadmap.
 * Run reproducible experiments and maintain rigorous evaluation to drive continuous improvement.


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