Apertus Engineer: Post-training

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

We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in Europe. The team counts more than a dozen full-time engineers working alongside leading researchers from EPFL and ETH Zürich, has released the Apertus 1 and Apertus 1.5 models, and works with over thirty academic collaborators to deliver fully open (open source), responsibly trained, multilingual, multimodal AI models for research and industry. Apertus is trained and developed on Alps, the Swiss National Supercomputing Centre's supercomputing infrastructure. The role requires someone who is comfortable working in an HPC environment and collaborating with researchers and infrastructure engineers.

• MSc or PhD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field

• Exceptional BSc candidates with strong engineering experience will also be considered

• Experience in AI and neural network architectures

• Strong collaboration and communication skills and ability to work across research and engineering teams

• Prior hands-on experience in the core domains of this role is required

• This can be project or study based experience; formal work experience is preferred

• A high degree of flexibility: priorities, tools, and day-to-day tasks shift with training schedules, releases, and a fast-moving field

• Hands-on experience with LLM post-training, be it alignment (SFT, preference optimisation) or reinforcement learning

• This means experience with frameworks such as veRL, slime, Megatron-LM, DeepSpeed, TRL, vLLM, SGLang, or similar tools

• Familiarity with distributed training concepts such as data parallelism, tensor parallelism, pipeline parallelism, checkpointing, and GPU communication

• Experience with Slurm or another HPC workload manager

• Experience building or adapting containers for HPC or GPU clusters

• Published research in the domains relevant to this role, or familiarity with recently published research on these topics

• Experience creating verifiable tasks for mathematics, code, reasoning, or tool use

• Familiarity with lower-level GPU/distributed libraries such as NCCL, Transformer Engine, FlashAttention, or communication backends

• Experience with large-scale evaluation pipelines