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

Our Mission Aleph 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 requirements, and work reliably in high-stakes settings. We're building that in Heidelberg. We are hiring an AI Researcher (f/m/d) 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. Team Culture 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. About the Role As an AI Researcher in Pre-training (f/m/d) , you will work on the technical levers that determine the success of our next-generation models: architecture, optimization, stability, and scaling. 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. 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. Your Responsibilities • Recipe & Architecture Experiments: 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. • Scaling Experiments: 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. • Run Monitoring & Debugging: Help monitor large-scale training runs and investigate convergence issues such as loss spikes and divergence. Work with the team to track down distributed training failures. • Research to Practice: Follow the literature, reproduce promising results at small scale, and bring ideas worth testing to the team. • Cross-Team Collaboration: Work with the Compute Performance, Data, Evaluation, and Post-Training teams to understand how hardware constraints and the rest of the model lifecycle shape pre-training decisions. Core Qualifications • You are proficient in Python and have hands-on experience with PyTorch. • You have a strong foundation in machine learning, shown through research (e.g., a PhD, a strong MS'c thesis, or publications), open-source contributions, or substantial professional or substantial personal projects. • You have a solid mathematical background (linear algebra, probability, optimization) and enjoy reasoning formally about why models