Apertus Engineer / Apertus Engineeress

Apertus Engineer: Post-training

100%, Zurich, fixed-term

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 transform Apertus base models into effective assistants. This role requires a strong background in large language model (LLM) post-training, solid software engineering skills, and the ability to work collaboratively in a research-focused high-performance computing (HPC) environment.

Project Background

We train open foundation models with hundreds of billions of parameters on thousands of GPUs using one of the largest AI-ready supercomputers in Europe. Our team consists of over a dozen full-time engineers working alongside leading researchers from EPFL and ETH Zürich. We have successfully released the Apertus 1 and Apertus 1.5 models and collaborate with more than thirty academic partners to deliver fully open (open source), responsibly trained, multilingual, multimodal AI models for research and industry.

Apertus is trained and developed on the Alps supercomputing infrastructure at the Swiss National Supercomputing Centre. The ideal candidate will be comfortable working in an HPC environment and collaborating with researchers and infrastructure engineers.

Job Description

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 reinforcement learning 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 utilization.
• Help maintain reproducible training recipes, configuration files, launch scripts, and documentation.
• Collaborate with researchers and CSCS engineers to enhance 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.
• Implement and run reward modeling, reward calibration, and verifier-based training.
• Generate and validate synthetic or gym training tasks.
• Conduct ablation studies comparing algorithms, reward functions, data mixtures, hyperparameters, and infrastructure settings.
• Evaluate model behavior across reasoning, coding, mathematics, instruction-following, multilingual capabilities, tool-use, and safety benchmarks.
• Debug common post-training issues, including optimization instability, reward hacking, regressions, and evaluation failures.

Profile

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, with the ability to work across research and engineering teams.
• Prior hands-on experience in the core domains of this role is required.
• A high degree of flexibility, as priorities, tools, and day-to-day tasks shift with training schedules and developments in a fast-moving field.
• Hands-on experience with LLM post-training techniques, including alignment (SFT, preference optimization) and reinforcement learning.
• Proficiency 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 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.

Workplace

The role can be based either in Lausanne at EPFL or in Zürich at ETH Zürich.

We Offer

• 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.

We Value Diversity and Sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity, and nurture a working and learning environment in which the rights and dignity of all staff and students are respected.

We are consistently working towards a climate-neutral future and believe in fostering an environment that allows everyone to grow and flourish.

Curious? So Are We.

Apply online using the form below. Only applications matching the job profile will be considered.

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 at ischlag@ethz.ch (no applications).

About ETH Zürich

ETH Zurich is one of the world’s leading universities specializing in science and technology. We are renowned for our excellent education, cutting-edge fundamental research, and direct transfer of new knowledge into society. With over 30,000 people from more than 120 countries, our university promotes independent thinking and fosters an environment that inspires excellence. Located in the heart of Europe, we are dedicated to developing solutions for the global challenges of today and tomorrow.