Apertus Engineer: Infrastructure

We are seeking a skilled infrastructure engineer to join the Apertus team. The ideal candidate will own the container image stack behind our pre-training, post-training, and serving workloads, and collaborate with CSCS engineers to keep large-scale training on Alps stable and fast. This role requires strong Linux and container skills, experience with HPC environments, and the ability to work collaboratively across research, engineering, and operations teams.

The Apertus project, a joint effort between EPFL, ETH Zürich, and CSCS, is seeking a practical and motivated infrastructure engineer to help build the next version of Apertus. The successful candidate will own the container image stack and work closely with CSCS to keep large-scale training stable and fast.

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 (CSCS) supercomputing infrastructure. The role requires someone who is comfortable working in an HPC environment and collaborating with researchers and infrastructure engineers.

The engineer will enable stability and throughput for the Apertus pre-training and post-training pipelines by maintaining the ML system images and partnering with CSCS on the underlying infrastructure.

ML system image maintenance

Build, maintain, and upgrade container images for all core ML development phases: pre-training, post-training/alignment, and model serving/deployment

Target the ARM-based (aarch64, Grace-Hopper) node architecture of Alps, managing the full dependency stack (CUDA, NCCL, PyTorch, training and serving frameworks)

Keep image builds reproducible, versioned, and documented, including CI for builds and upgrades

Validate images against reference pre-training and post-training workloads together with Apertus engineers, and maintain working launch examples

Compute partnership and efficiency

Serve as the primary technical point of contact with CSCS engineers and researchers regarding compute, reliability, and efficiency

Work collaboratively with CSCS staff to identify and implement improvements in the efficiency and performance of the underlying compute infrastructure, overlapping with ML systems performance engineering

Contribute to systemic improvements in CSCS-based resources (network, storage, scheduling) relevant to large-scale LLM training

Document and disseminate institutional knowledge about the CSCS infrastructure and best practices for leveraging these high-performance systems

Infrastructure stress testing

Stress test the infrastructure using representative pre-training and post-training workloads, building on the project's existing recipes and examples, to validate stability and throughput after image upgrades, system maintenance, and configuration changes

Work closely with Apertus pre-training and post-training engineers to debug cluster-level problems affecting stability and throughput: node failures, networking, storage performance, checkpointing, and scheduling

Support the Apertus serving stack, which builds on the same images (operation of the serving stack is owned by a separate engineer)

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

Hands-on experience with HPC environments: job schedulers such as Slurm, shared filesystems, and multi-node GPU systems

Strong Linux systems and container skills (Docker/Podman and HPC runtimes such as enroot or Apptainer)

Strong collaboration and communication skills and ability to work across research, engineering, and operations 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

Strongly preferred

Familiarity with LLM training and serving frameworks such as Megatron-LM, PyTorch distributed, vLLM, or SGLang

Experience building or adapting containers for ARM64/aarch64 platforms

Familiarity with HPC networking and communication stacks: Slingshot, libfabric, NCCL and its debugging

Experience with parallel filesystems (e.g. Lustre) and storage performance tuning

Experience setting up CI/CD pipelines for container image builds

Nice to have

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

Experience profiling distributed GPU workloads (Nsight, DCGM, communication benchmarks) and translating findings into infrastructure improvements

Experience with Grace-Hopper (GH200) or other tightly coupled CPU-GPU architectures

Contributions to open-source infrastructure or ML tooling

A stimulating academic environment at one of the world's leading technical universities

Access to Alps, one of the largest AI-ready supercomputers in Europe

The opportunity to work alongside and intersect with leading researchers in the field

Collaboration with top researchers and engineers from EPFL, ETH Zürich, CSCS, and other Swiss institutions

Attractive employment conditions and comprehensive benefits, including the ETH Zürich/EPFL pension plans

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

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.