Apertus Engineer: Post-training

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 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.

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
Essential

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
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
Nice to have

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
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
We look forward to receiving your online application with the following documents:

CV/Resume
Cover letter explaining your interest and qualifications
Academic transcripts
Contact information for 2-3 references
Links to GitHub repositories or other examples of your programming work (if available)
Further information about the ETH AI Center and the Swiss AI Initiative can be found on our website. Questions regarding the position should be directed to Dr. Imanol Schlag, email ischlag@ethz.ch (no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

For recruitment services the GTC of ETH Zurich apply.