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Our Mission Aleph Alpha is one of the few companies in Europe doing serious foundation model pre-training. Our customers — in finance, manufacturing, and 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 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 about designing inference-efficient architectures, optimising 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 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 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 Role As 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. Responsibilities Recipe & Architecture Optimization: Own core elements of the training recipe (optimizers, schedules, initialization) and design PyTorch-based architectural improvements to maximize convergence, stability, and training efficiency. Scaling Strategy & Predictability: Develop hyperparameter scaling laws and scale-up methodologies, using small-scale proxy experiments to reliably predict multi-thousand-GPU behavior and de-risk major training decisions. Stability, Diagnostics & Debugging: Investigate complex convergence issues (loss spikes, divergence) and resolve hard-to-reproduce distributed system failures like communication bottlenecks, race conditions, and synchronization errors. System-Model Co-Design: Partner with Compute Performance, Data, Evaluation, and Post-Training teams to align the model lifecycle with hardware constraints, memory bandwidth, and communication topologies. Qualifications You are proficient in Python and deeply familiar with PyTorch-based training workflows. You have a strong track record in machine learning research and software engineering, demonstrated through shipped models, impactful open-source contributions, or published research. You have a strong mathematical foundation and are comfortable reasoning formally about optimisation, scaling behaviour, and training dynamics. You deeply understand transformer training dynamics, optimisation, and the behaviour of large distributed training jobs. You can design rigorous experiments, reason clearly from noisy results, and translate empirical observations into robust training decisions. , 100+ GPU clusters). You apply strong software engineering practices, including writing maintainable, well-tested code and supporting reproducible experimentation workflows. You are able to implement complex model architectures efficiently and reliably and to debug complex issues across model code, training dynamics, and distributed systems.

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

Don't apply unprepared

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

Don't apply unprepared