Post-Training Engineer - Apertus

EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries.

The Apertus project, a joint effort between EPFL and ETH Zürich, is seeking a practical and motivated engineer to help build the next generation of open foundation models. The successful candidate will help develop and run post‑training and reinforcement learning pipelines for the Apertus project.

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.

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

Infrastructure and systems engineering

• Build and maintain containerized 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, file system performance, networking, and GPU utilization.
• 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 optimization, and reinforcement learning workflows.
• Build and run RL environments for tasks with verifiable outcomes, such as mathematics, code, tool‑use, and reasoning.
• Develop reward modeling, 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 behavior across reasoning, coding, mathematics, instruction‑following, multilingual, tool‑use, and safety benchmarks.
• Debug common post‑training issues, including optimization instability, reward hacking, regressions, and evaluation failures.

• 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.

Strongly preferred

• Experience with Slurm or another HPC workload manager.
• Experience building or adapting containers for HPC or GPU clusters.
• Experience with LLM fine‑tuning, post‑training, preference optimization, or reinforcement learning.
• Familiarity with distributed training concepts such as data parallelism, tensor parallelism, pipeline parallelism, checkpointing, and GPU communication.
• Experience with frameworks such as veRL, slime, Megatron‑LM, Deep Speed, TRL, vLLM, SGLang, or similar tools.

Nice to have

• Experience with RL for LLMs, online policy optimization, reward modeling, or RLVR.
• Experience creating verifiable tasks for mathematics, code, reasoning, or tool use.
• Familiarity with lower‑level GPU/distributed libraries such as NCCL, Transformer Engine, Flash Attention, or communication backends.
• Experience with large‑scale evaluation pipelines.

• 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 specialized 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.

Contract Start Date : as soon as possible