PhD Position

Next-Generation Data Systems for Multimodal AI Applications

Ref. 2026_010

Location: IBM Research Europe, Switzerland

Funding: Fully funded by the Swiss National Science Foundation (SNSF)

Academic partners: Zurich University of Applied Science and PhD via University of Zurich

Introduction

Recent advances in AI are transforming how complex applications are built. Modern systems increasingly combine large language models (LLMs), vision models, vector search, retrieval systems, and large-scale data analytics into integrated pipelines. These multimodal workloads are executed across a heterogeneous ecosystem of processing engines and hardware accelerators, including distributed data processing frameworks, ML runtimes, vector databases, and GPU-based inference systems.

Supporting these workloads efficiently requires a new generation of data systems capable of orchestrating diverse components across heterogeneous engines and hardware.

At IBM Research Europe - Zurich, we are investigating the foundations of next-generation systems for multimodal AI applications. This includes architectures that can dynamically route tasks across heterogeneous engines, optimize execution strategies, and efficiently manage compute resources across CPUs, GPUs, and specialized hardware accelerators.

This PhD project focuses on systems for multimodal AI workflows, exploring how complex pipelines involving LLM reasoning, vision models, and data analytics can be automatically optimized and executed across multiple engines.




Your Role and Responsibilities

As a PhD researcher, you will design and build next-generation systems for multimodal AI infrastructure. Your work may include:

• Developing architectures that support multimodal AI pipelines.
• Designing cross-engine workflow routing mechanisms that dynamically select the best execution engine for each component of a workload.
• Investigating optimization techniques for AI pipelines, including analytical- and learning-based optimizers for heterogeneous systems such as relational databases (e.g. PostgreSQL, DuckDB) and distributed data processing frameworks (e.g. Spark, Presto).
• Building resource management and scheduling mechanisms for heterogeneous hardware environments (CPUs, GPUs, hardware accelerators).
• Enabling efficient execution of AI workflows, where multiple models and tools interact dynamically to solve complex tasks.
• Implementing prototype systems and evaluating them on realistic AI and data workloads.

The research will bridge ideas from database systems, distributed systems, and machine learning systems.





Your Profile

We are looking for highly motivated candidates interested in systems research for supporting multimodal AI applications.

Required qualifications

• Master’s degree in Computer Science, AI, Data Engineering, or a related field
• Strong background in distributed systems, databases, or machine learning systems
• Solid programming skills (e.g., C++, Rust, Java, or Python)
• Interest in publishing in leading systems and AI systems conferences such as VLDB, SIGMOD, CIDR, EuroSys, OSDI, or MLSys

Preferred experience

• Relational databases (e.g. PostgreSQL, MariaDB, DuckDB) and distributed data processing frameworks (e.g., Spark, Ray, Flink)
• Query processing or system optimization
• ML infrastructure or LLM systems
• Resource scheduling or cluster management
• GPU or accelerator- aware systems

What We Offer

This position provides the opportunity to:

• Conduct cutting-edge research at the intersection of systems and AI
• Work closely with researchers at IBM Research Europe - Zurich, the Zurich University of Applied Sciences and the University of Zurich
• Publish in top-tier systems and systems for AI venues
• Work in a highly collaborative international research environment in Zurich
• Translate research ideas into impactful systems used in real-world applications

Diversity & Work Environment

IBM is committed to fostering diversity and inclusion in the workplace. You will join an open, multicultural research environment that values different perspectives and supports flexible working arrangements. Our goal is to help all genders and backgrounds thrive professionally while maintaining a healthy work-life balance.

How to Apply

If you are interested in this position, please submit your application through the button below.