The engineer will contribute to the development, execution, and evaluation of scalable post-training workflows for Apertus.

Infrastructure and systems engineering

• Build and maintain containerised environments for LLM post-training and RL workloads
• Adapt containers and dependencies for execution on Alps / CSCS infrastructure
• Run and monitor Slurm-based training and evaluation jobs
• Debug failures related to distributed execution, checkpointing, filesystem performance, networking, and GPU utilisation
• Help maintain reproducible training recipes, configuration files, launch scripts, and documentation
• Work with researchers and CSCS engineers to improve the reliability and performance of large-scale experiments

LLM post-training and reinforcement learning

• Support SFT, preference optimisation, and reinforcement learning workflows
• Build and run RL environments for tasks with verifiable outcomes, such as mathematics, code, tool-use, and reasoning
• Implement and run reward modelling, reward calibration, and verifier-based training
• Generate and validate synthetic or gym training tasks
• Run ablation studies comparing algorithms, reward functions, data mixtures, hyperparameters, and infrastructure settings
• Evaluate model behaviour across reasoning, coding, mathematics, instruction-following, multilingual, tool-use, and safety benchmarks
• Debug common post-training issues, including optimisation instability, reward hacking, regressions, and evaluation failures