To support teaching, research and knowledge transfer, the IT Services (ITS) of ETH Zurich are working as a service organization in a large and complex IT environment. Within the ITS section Scientific IT Services the group Computational & Data Science Support (CDSS) aims at bridging the gap between computational research and IT services and infrastructure provisioning. We are working closely together with ETH researchers to provide support in the wide area of scientific computing from data management and analysis, development of scientific software, to porting and optimizing such software to high-performance and cloud computing infrastructures.

Project background

We are looking for a Data Science Expert with a strong background in GenAI technologies to join ETH's Scientific IT Services. The primary focus will be on ETH's recent initiative to build a generic, scalable and extensible "AI platform" with services and functionalities for teaching, research and administration. Additionally, this role will encompass work on other ML and scientific computing projects, providing opportunities to support diverse research and computational needs across the institution.

Job description

• Your primary focus will be on ETH's AI platform project, where you will gather requirements, conduct feasibility studies, and perform technical assessments for GenAI solutions across the domains of teaching, research, and administration.
• You will develop prototypes of GenAI workflows customized to client needs, including Retrieval Augmented Generation (RAG) bots and AI Agents.
• You will document and help integrate these prototypes and new functionalities into ETH's AI platform.
• You will also investigate emerging tools and technologies relevant to the platform's evolution.
• Beyond the AI platform, you will contribute to diverse machine learning projects across ETH's research and administrative domains, developing and implementing scientific computing solutions to support various projects.
• Throughout all your work, you will prepare and present teaching materials on GenAI best practices and broader ML topics.
• Your role also requires effectively communicating with and managing relationships across diverse stakeholders (e.g., Educational IT Services, Unit for Teaching and Learning, ETH AI Center, ETH Library), from technical experts to non-technical users.

This position requires

• PhD degree in data / computational sciences or related field
• Good understanding of the mathematical and numerical foundations of machine learning, statistical analysis and modeling
• Proficiency in Python and its ML tech stack / libraries such as TensorFlow, PyTorch and scikit-learn
• Experience with GenAI frameworks and orchestration tools such as LangChain, LangGraph, LlamaIndex, or similar libraries for building LLM applications
• Working and development experience with various model architectures such as transformers, as well as classical ML approaches
• Experience in building chatbots, conversational agents or RAG (Retrieval-Augmented Generation) systems and related technologies such as embeddings or vector databases/extensions (Milvus, Chroma, pgvector, etc.)
• Experience in applying software engineering best practices including automated testing, version control (Git), CI/CD pipelines, and containerization technologies (e.g., Docker)
• Ability to work effectively across diverse technical projects and adapt methodologies to different scientific computing challenges

Advantageous are

• Good understanding of prompt engineering and prompt optimization strategies
• Experience with cloud platforms (e.g., Azure) and their ML services
• Previous working experience in developing / applying GenAI technologies in the areas of teaching and learning
• Understanding of responsible AI / AI ethics such as mitigation, hallucination detection, safety filters, interpretability

In addition to the above analytical and technical abilities, this position demands a strong interest in and ability to work with experts from various fields of expertise (also non-technical ones) and translating their requirements to solutions. This requires very good communication skills in English (German is an asset), solid project management skills and a service-oriented attitude to effectively establish a fruitful and successful collaboration with our partners.

The position is located in Zurich, though occasional travel to other Swiss cities will be required.

We offer

• Your job with impact: Become part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society.
• This position is a unique working opportunity to contribute to an exciting ETH initiative of building a state-of-the-art AI platform aimed at providing useful and powerful GenAI services to the diverse academic community of ETH Zurich.
• Scientific IT Services provide a stimulating work environment for knowledge sharing and exchange and possibilities for engagement around a vast spectrum of areas in scientific IT as well as for further education.
• We are committed to flexible and family-friendly working models and a healthy work-life balance.
• ETH Zurich supports a policy of diversity as well as inclusion and we particularly encourage female candidates to apply.
• You can expect numerous benefits, such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ, childcare and attractive pension benefits.

This contract will be for a period of one (1) year, with possibility of renewal subject to budgetary considerations.

Curious? So are we.

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

• Motivation letter
• CV
• Diplomas and work certificates
• Contact information of at least two references

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

Further information about Scientific IT Services can be found here. Questions regarding the position should be directed to Dr. Tarun Chadha at [email protected] or Dr. Thomas Wüst at [email protected] (no applications).

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.