Apertus Engineer: Deployment

We are seeking a skilled engineer to own the technical release path of Apertus models. The ideal candidate will integrate Apertus into the open-source inference ecosystem, produce quantised variants, and make sure each release works out of the box for the community. This role requires strong Python and software engineering skills, experience with LLM inference stacks, and a track record of open-source contributions.

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. This role sits at the interface between the training team and the open-source community: the social side of community engagement is owned by our community manager, while this role owns the technical release path.

The engineer will make Apertus releases work out of the box across the open-source LLM ecosystem, from server-grade inference to personal deployment.

Upstream integration and release engineering

Manage the technical release path of trained Apertus models: checkpoint conversion and preparation of release artefacts (weights, configurations, tokenisers, model cards) together with the training team
Implement and upstream support for Apertus model architectures in community libraries such as Hugging Face Transformers, vLLM, SGLang, and llama.cpp, and shepherd these contributions through review so community support is a given
Verify day-0 compatibility of new releases with the major inference engines and model formats
Coordinate release timing and technical materials with the community manager
Quantisation

Produce quantised variants of released models (e.g. FP8, INT4/AWQ/GPTQ, GGUF) suitable for server and personal deployment
Validate quantised variants against evaluation benchmarks to ensure quality is preserved
Documentation and examples

Provide example scripts and reference configurations showing how to serve and use Apertus models with vLLM, SGLang, and Transformers
Support personal and local deployment ecosystems such as LM Studio, Ollama, and llama.cpp
Maintain deployment documentation and troubleshooting guides for the community
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
Strong Python and software engineering skills, including experience with open-source contribution workflows (pull requests, code review, CI)
Experience with LLM inference stacks such as Hugging Face Transformers, vLLM, or SGLang
Strong collaboration and communication skills and ability to work across research, engineering, and community-facing 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
A track record of merged contributions to ML or inference libraries (e.g. Transformers, vLLM, SGLang, llama.cpp)
Strongly preferred

Experience converting models between formats and frameworks (e.g. Megatron-LM checkpoints, safetensors, GGUF)
Familiarity with personal and local deployment tools such as LM Studio, Ollama, or llama.cpp
Experience writing developer-facing documentation and example code
Nice to have

Published research in the domains relevant to this role, or familiarity with recently published research on these topics
Experience quantising models without performance degradation (FP8, INT4, AWQ, GPTQ) and evaluating quantised models
Experience with LLM evaluation harnesses and benchmark pipelines
Familiarity with GPU inference performance tuning and serving at scale
Experience with Apple Silicon / MLX or other consumer-hardware inference targets
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.