We are seeking a skilled engineer to join the Apertus post-training effort. The ideal candidate will develop, run, and evaluate the SFT and reinforcement learning pipelines used to turn Apertus base models into capable assistants. This role requires a strong background in LLM post-training, solid software engineering skills, and the ability to work collaboratively in a research-focused HPC environment.
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 works with over thirty academic collaborators to deliver fully open, responsibly trained, multilingual, multimodal AI models for research and industry.
Tasks
- 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.
Requirements
- 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; experience with frameworks such as veRL, slime, Megatron-LM, DeepSpeed, TRL, vLLM, SGLang, or similar tools strongly preferred.
- 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 (nice to have).
- Experience creating verifiable tasks for mathematics, code, reasoning, or tool use (nice to have).
- Familiarity with lower-level GPU/distributed libraries such as NCCL, Transformer Engine, FlashAttention, or communication backends (nice to have).
- Experience with large-scale evaluation pipelines (nice to have).
Benefits
- A stimulating academic environment at one of the world's leading technical universities.
- The opportunity to work with state-of-the-art supercomputing infrastructure and cutting-edge AI research.
- Collaboration with top researchers and engineers from EPFL, ETH Zürich, CSCS, and other Swiss institutions.
- Flexible working arrangements, including options for remote work.
- Professional development opportunities, including conference attendance and specialised training.
- The chance to contribute to open-source projects with global impact.
- Access to the broader Swiss academic ecosystem and industry partnerships.
- Being part of Switzerland's sovereign AI development, working on technology with national significance.
- The role can be based either in Lausanne at EPFL or in Zürich at ETH Zürich.
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Über uns
Das Team
The team includes more than a dozen full-time engineers working alongside leading researchers from EPFL and ETH Zürich, and collaborates with over thirty academic partners. Work is conducted on Alps, the Swiss National Supercomputing Centre's infrastructure, in a research-focused HPC environment.