At ZEISS Corporate Research & Technology, we work at the frontier of science and technology to identify emerging computing architectures and translate them into future technology opportunities for ZEISS.

We are looking for a Research Scientist who understands accelerator architectures and fundamentally new computing concepts driven by the latest AI developments – from CPUs, GPUs and TPUs to custom accelerators, optical and analog computing, novel memory architectures and new forms of heterogeneous computing.

The position combines strategic technology exploration with hands-on technical work. You will build targeted proof-of-concepts to validate promising architecture concepts, while engaging with startups, research groups and technology partners to understand emerging solutions first-hand, assess their maturity and identify the most promising approaches for ZEISS

Your role

• Analyze AI computing systems end-to-end: Understand how training and inference workloads run on modern data-center architectures and identify the fundamental bottlenecks in compute, memory, interconnects and energy efficiency.
• Explore next-generation computing architectures: Evaluate established, emerging and fundamentally new concepts – custom accelerators, chiplets, in- and near-memory computing, optical and analog computing and heterogeneous systems.
• Build and benchmark proof-of-concepts: Translate architectural hypotheses into hands-on demonstrators and assess their potential quantitatively against the state of the art.
• Scout and recommend: Engage with startups, research groups and technology providers, judge maturity and differentiation, and distinguish fundamental technology shifts from incremental improvements.

Your profile

• A university degree in computer science, computer engineering, electrical engineering with a focus on digital systems or computer architecture, or a closely related discipline. A PhD or equivalent research experience is welcome.
• Expertise in at least two of the following areas: computer architecture and systems; machine-learning systems and AI infrastructure; heterogeneous accelerators and emerging computing architectures.
• A systems-level understanding of modern AI technology stacks – training and inference workflows, numerical kernels, memory hierarchies, data movement, interconnects, accelerator hardware and system software – and the ability to explain where bottlenecks occur, why, and which architectural approaches can address them.
• Programming skills in Python and/or C/C++; experience with GPU computing, parallel programming, distributed systems, compilers, runtimes or HPC environments is an advantage.
• Curiosity and openness toward external technology developments, and the ease to engage directly with startups, research groups and technology providers, understand their approaches in technical depth and assess their relevance.
• An analytical eye for technology trends, the flexibility to revise your view when the evidence changes, and the ability to present complex topics clearly. Business-fluent English is required.

Your ZEISS Recruiting Team:

Friederike Kirklar-Harms