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.

Your 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.

Core 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.

• Hands-on experience pre-training large models (e.g., 7B+ parameters) on substantial infrastructure (e.g., 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.

• You collaborate effectively within a research and engineering team and communicate clearly about your work across Pre-training and the broader AAR/AA organization.

• You are able to work in Germany and collaborate regularly on site in Heidelberg as part of the Pre-training team.

Preferred Qualifications

• Large-Scale Training: Hands-on experience training LLMs or multimodal models on large GPU clusters using distributed frameworks (e.g., Megatron-LM, DeepSpeed, torchtitan).

• Predictive Scaling: Familiarity with scaling laws, hyperparameter transfer, or methods for predicting large-scale training behavior from smaller proxy runs.

• Stability & Performance: Experience profiling distributed jobs and diagnosing training anomalies like loss spikes, numerical instability, or optimizer pathologies.

• Advanced Architectures: Exposure to sparse training approaches (e.g., Mixture-of-Experts) and an understanding of their routing and systems trade-offs.

• Track Record of Impact: Demonstrated research excellence through top-tier publications (NeurIPS, ICML, ICLR), impactful open-source contributions, or significant shipped technical work.

• Systems Curiosity: Low-level kernel optimization is not required, but we highly value a strong curiosity about the hardware and systems constraints that shape scale.

What we offer

• Become part of an AI revolution!

• 30 days of paid vacation

• Access to a variety of fitness & wellness offerings via Wellhub

• Mental health support through nilo.health

• Substantially subsidized company pension plan for your future security

• Subsidized Germany-wide transportation ticket

• Budget for additional technical equipment

• Flexible working hours for better work-life balance and hybrid working model

• JobRad® Bike Lease