Apertus Engineer: Deployment

Poste

Temps plein

Durée déterminée

Mid

Ingénierie

Modalités

Lausanne, Vaud

Sur site

100%

Exigences

Master

Compétences

llama.cppSGLangINT4/AWQ/GPTQFP8Checkpoint conversionPythonGGUFQuantisationOpen-source contributionHuggingFaceNeural Network ArchitecturesDeployment

Langues

Anglais

Description

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

Responsabilités

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
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
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

Exigences

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)

Technologies

PythonHugging Face TransformersvLLMSGLangllama.cpp

Avantages

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

Entreprise

ETH Zurich

Education

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

Contact

Dr. Imanol Schlag

ischlag@ethz.ch