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Senior AI Researcher- Pre-training (f/m/d)

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EMP:Technology
Heidelberg
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Descripción

Our MissionAleph Alpha is one of the few companies in Europe doing serious foundation model pre-training like our recently released Kolibri-1 model.

Our customers - in finance, manufacturing, public administration - need models that understand German, meet European regulatory.

Requisitos

, and work reliably in high-stakes settings

We're building that in Heidelberg.

We are hiring a Senior AI Researcher to join our Pre-training team and to advance the architecture and training of our next generation of foundation models.

If you are excited.

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designing inference-efficient architectures, optimizing training recipes that scale reliably, and training models on a large scale cluster (thousands of NVIDIA Blackwell GPUs), we would love to hear from you.Team CultureWe foster a culture built on ownership, autonomy, and empowerment

Teams and individual contributors are trusted to take responsibility for their work and drive meaningful impact.

We maintain a flat organisational structure with efficient, supportive management that enables quick decision-making, open communication, and a strong sense of shared purpose.

We collaborate closely on complex technical problems, working in pairs or using mob programming to resolve challenging issues.

About the RoleAs a Senior AI Researcher in Pre-training (f/m/d), you will own the critical technical levers that determine the success of our next-generation models: architecture, optimization, stability, and scaling.

Working at the high-leverage intersection of research and engineering, you will translate mathematical reasoning and empirical observations into principled training decisions - from small-scale proxy experiments to multi-thousand-GPU runs.

We are looking for an expert who can combine rigorous experimental design with high-quality production code, directly influencing model quality, run reliability, and the efficiency of the models we ship.

Your ResponsibilitiesRecipe & Architecture Optimization: Own core elements of the tr.

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