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

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 train the way they do.

You understand how transformers work and have pre-trained LLMs from scratch yourself, even at small scale.

You design experiments carefully, report noisy and negative results honestly, and can explain clearly what your evidence does and does not show.

You have a track record of picking up unfamiliar technical areas quickly and going deep, for example by reimplementing papers, ramping up on a large new codebase, or debugging a problem well outside your comfort zone.

You write clean, maintainable code and care about reproducible experiments.

You communicate clearly, raise problems early, and actively seek out and act on feedback.

Distributed Training: Experience training models on multi-GPU or multi-node setups, or familiarity with distributed frameworks (e.g., Megatron-LM, DeepSpeed, torchtitan) and parallelism strategies (data, tensor, pipeline, expert).

Training Dynamics: Familiarity with scaling laws, hyperparameter transfer (e.g., µP), or recent work on optimizers and training stability.

Efficient Architectures: Exposure to Mixture-of-Experts, efficient attention variants, or other inference-efficient designs.

Research Output: Publications at top-tier venues (NeurIPS, ICML, ICLR) or contributions to open-source ML training libraries.

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.