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AI Researcher - Pre-training (f/m/d)
<p style="min-height:1.5em"><strong>Our Mission</strong><br />Aleph Alpha is one of the few companies in Europe doing serious foundation model pre-training like our recently released <a target="_blank" rel="noopener noreferrer" href="https://huggingface.co/Aleph-Alpha/Kolibri-1">Kolibri-1 model</a>. Our customers - in finance, manufacturing, public administration - need models that understand German, meet European regulatory requirements, and work reliably in high-stakes settings. We're building that in Heidelberg.</p><p style="min-height:1.5em">We are hiring an <strong>AI Researcher (f/m/d)</strong> to join our Pre-training team and help advance the architecture and training of our next generation of foundation models. If you are excited about how large models learn, want to run experiments that feed directly into production training runs, and want hands-on time on large-scale clusters (thousands of NVIDIA Blackwell GPUs), we would love to hear from you.</p><p style="min-height:1.5em"><strong>Team Culture</strong><br />We 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 organizational 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. New team members learn through real work: from your first weeks you will pair with experienced researchers on live problems, and take on more ownership as you grow into it.</p><p style="min-height:1.5em"><strong>About the Role</strong><br />As an<strong> AI Researcher in Pre-training (f/m/d)</strong>, you will work on the technical levers that determine the success of our next-generation models: architecture, optimization, stability, and scaling.</p><p style="min-height:1.5em">Working at the intersection of research and engineering, you will learn to translate mathematical reasoning and empirical observations into principled training decisions, from small-scale proxy experiments to multi-thousand-GPU runs. You will start with well-scoped problems alongside senior researchers and progressively take ownership of larger parts of the training recipe.</p><p style="min-height:1.5em">We are looking for someone with strong fundamentals, sharp experimental instincts, exceptional skills, and the drive to go deep fast. You learn as much from a failed run as from a paper, you write code others can build on, and you want to become an expert in large-scale pre-training.</p><p style="min-height:1.5em"><strong>Your Responsibilities</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em"><strong>Recipe & Architecture Experiments:</strong> Design and run ablations on core elements of the training recipe (optimizers, schedules, initialization), and implement PyTorch-based architectural changes. Evaluate their effect on convergence, stability, and training efficiency.</p></li><li><p style="min-height:1.5em"><strong>Scaling Experiments:</strong> Run and analyze small-scale proxy experiments, and contribute to the hyperparameter scaling laws and scale-up methods we use to predict behavior at multi-thousand-GPU scale.</p></li><li><p style="min-height:1.5em"><strong>Run Monitoring & Debugging:</strong> Help monitor large-scale training runs and investigate convergence issues such as loss spikes and divergence